Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

On the Optimization of Aerospace Plane Ascent Trajectory

NASA Astrophysics Data System (ADS)

Al-Garni, Ahmed; Kassem, Ayman Hamdy

A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

A hybrid genetic algorithm for solving bi-objective traveling salesman problems

NASA Astrophysics Data System (ADS)

Ma, Mei; Li, Hecheng

2017-08-01

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, in a traditional TSP only tour distance is taken as a unique objective to be minimized. When more than one optimization objective arises, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is taken into account, where both the distance and the cost are taken as optimization objectives. In order to efficiently solve the problem, a hybrid genetic algorithm is proposed. Firstly, two satisfaction degree indices are provided for each edge by considering the influences of the distance and the cost weight. The first satisfaction degree is used to select edges in a “rough” way, while the second satisfaction degree is executed for a more “refined” choice. Secondly, two satisfaction degrees are also applied to generate new individuals in the iteration process. Finally, based on genetic algorithm framework as well as 2-opt selection strategy, a hybrid genetic algorithm is proposed. The simulation illustrates the efficiency of the proposed algorithm.

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

NASA Astrophysics Data System (ADS)

Wang, Aimeng; Guo, Jiayu

2017-12-01

A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster

NASA Technical Reports Server (NTRS)

Story, George

2015-01-01

Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.

Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster

NASA Technical Reports Server (NTRS)

Story, George

2014-01-01

Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and later on solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. A remaining issue is the cost of hybrids vs the existing launch propulsion systems. This paper will review the known state of the art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.

Solving deterministic non-linear programming problem using Hopfield artificial neural network and genetic programming techniques

NASA Astrophysics Data System (ADS)

Vasant, P.; Ganesan, T.; Elamvazuthi, I.

2012-11-01

A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.

Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm

NASA Astrophysics Data System (ADS)

Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian

2017-03-01

DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Furthermore, the hybrid clustering is applied by using implementation of Parallel K-Means algorithm and DIANA algorithm. The aim of using Hybrid Clustering is to obtain better clusters result. For validating the resulted clusters, to get optimum number of clusters, we use Davies-Bouldin Index (DBI). In this study, the result of implementation of Parallel K-Means clustering is data clustered become 5 clusters with minimal IDB value is 0.8741, and Hybrid Clustering clustered data become 13 sub-clusters with minimal IDB values = 0.8216, 0.6845, 0.3331, 0.1994 and 0.3952. The IDB value of hybrid clustering less than IBD value of Parallel K-Means clustering only that perform at 1ts stage. Its means clustering using Hybrid Clustering have the better result to clustered DNA sequence of HPV than perform parallel K-Means Clustering only.

Hybrid Architectures for Evolutionary Computing Algorithms

DTIC Science & Technology

2008-01-01

other EC algorithms to FPGA Core Burns P1026/MAPLD 200532 Genetic Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based...on Parallel and Distributed Processing (IPPS/SPDP 󈨦), pp. 316-320, Proceedings. IEEE Computer Society 1998. [12] Scott, S. D. , Samal , A., and...Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based Genetic Algorithm”, Proceedings of the 1995 ACM Third

An application of traveling salesman problem using the improved genetic algorithm on android google maps

NASA Astrophysics Data System (ADS)

Narwadi, Teguh; Subiyanto

2017-03-01

The Travelling Salesman Problem (TSP) is one of the best known NP-hard problems, which means that no exact algorithm to solve it in polynomial time. This paper present a new variant application genetic algorithm approach with a local search technique has been developed to solve the TSP. For the local search technique, an iterative hill climbing method has been used. The system is implemented on the Android OS because android is now widely used around the world and it is mobile system. It is also integrated with Google API that can to get the geographical location and the distance of the cities, and displays the route. Therefore, we do some experimentation to test the behavior of the application. To test the effectiveness of the application of hybrid genetic algorithm (HGA) is compare with the application of simple GA in 5 sample from the cities in Central Java, Indonesia with different numbers of cities. According to the experiment results obtained that in the average solution HGA shows in 5 tests out of 5 (100%) is better than simple GA. The results have shown that the hybrid genetic algorithm outperforms the genetic algorithm especially in the case with the problem higher complexity.

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

2005-01-01

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

Genetic algorithm for neural networks optimization

NASA Astrophysics Data System (ADS)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

CARHTA GENE: multipopulation integrated genetic and radiation hybrid mapping.

PubMed

de Givry, Simon; Bouchez, Martin; Chabrier, Patrick; Milan, Denis; Schiex, Thomas

2005-04-15

CAR(H)(T)A GENE: is an integrated genetic and radiation hybrid (RH) mapping tool which can deal with multiple populations, including mixtures of genetic and RH data. CAR(H)(T)A GENE: performs multipoint maximum likelihood estimations with accelerated expectation-maximization algorithms for some pedigrees and has sophisticated algorithms for marker ordering. Dedicated heuristics for framework mapping are also included. CAR(H)(T)A GENE: can be used as a C++ library, through a shell command and a graphical interface. The XML output for companion tools is integrated. The program is available free of charge from www.inra.fr/bia/T/CarthaGene for Linux, Windows and Solaris machines (with Open Source). tschiex@toulouse.inra.fr.

Optimization of beam orientation in radiotherapy using planar geometry

NASA Astrophysics Data System (ADS)

Haas, O. C. L.; Burnham, K. J.; Mills, J. A.

1998-08-01

This paper proposes a new geometrical formulation of the coplanar beam orientation problem combined with a hybrid multiobjective genetic algorithm. The approach is demonstrated by optimizing the beam orientation in two dimensions, with the objectives being formulated using planar geometry. The traditional formulation of the objectives associated with the organs at risk has been modified to account for the use of complex dose delivery techniques such as beam intensity modulation. The new algorithm attempts to replicate the approach of a treatment planner whilst reducing the amount of computation required. Hybrid genetic search operators have been developed to improve the performance of the genetic algorithm by exploiting problem-specific features. The multiobjective genetic algorithm is formulated around the concept of Pareto optimality which enables the algorithm to search in parallel for different objectives. When the approach is applied without constraining the number of beams, the solution produces an indication of the minimum number of beams required. It is also possible to obtain non-dominated solutions for various numbers of beams, thereby giving the clinicians a choice in terms of the number of beams as well as in the orientation of these beams.

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

NASA Astrophysics Data System (ADS)

Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang

2016-09-01

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

PubMed Central

Ahmed, Zakir Hussain

2014-01-01

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

NASA Astrophysics Data System (ADS)

Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.

2015-08-01

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

PubMed

Akbar, Shahid; Hayat, Maqsood; Iqbal, Muhammad; Jan, Mian Ahmad

2017-06-01

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

NASA Astrophysics Data System (ADS)

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

PubMed Central

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889

An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems

PubMed Central

Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun

2014-01-01

An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

PubMed Central

Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah

2015-01-01

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974

A Gradient Taguchi Method for Engineering Optimization

NASA Astrophysics Data System (ADS)

Hwang, Shun-Fa; Wu, Jen-Chih; He, Rong-Song

2017-10-01

To balance the robustness and the convergence speed of optimization, a novel hybrid algorithm consisting of Taguchi method and the steepest descent method is proposed in this work. Taguchi method using orthogonal arrays could quickly find the optimum combination of the levels of various factors, even when the number of level and/or factor is quite large. This algorithm is applied to the inverse determination of elastic constants of three composite plates by combining numerical method and vibration testing. For these problems, the proposed algorithm could find better elastic constants in less computation cost. Therefore, the proposed algorithm has nice robustness and fast convergence speed as compared to some hybrid genetic algorithms.

Optimizing coherent anti-Stokes Raman scattering by genetic algorithm controlled pulse shaping

NASA Astrophysics Data System (ADS)

Yang, Wenlong; Sokolov, Alexei

2010-10-01

The hybrid coherent anti-Stokes Raman scattering (CARS) has been successful applied to fast chemical sensitive detections. As the development of femto-second pulse shaping techniques, it is of great interest to find the optimum pulse shapes for CARS. The optimum pulse shapes should minimize the non-resonant four wave mixing (NRFWM) background and maximize the CARS signal. A genetic algorithm (GA) is developed to make a heuristic searching for optimized pulse shapes, which give the best signal the background ratio. The GA is shown to be able to rediscover the hybrid CARS scheme and find optimized pulse shapes for customized applications by itself.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search.

PubMed

Villagra, Andrea; Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

PubMed

Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

2018-06-04

Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu\\xA0search

NASA Astrophysics Data System (ADS)

Yeh, Cheng-Ta; Lin, Yi-Kuei; Yang, Jo-Yun

2018-07-01

Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reliability maximization by finding the optimal multi-state resource assignment, which is one resource to each arc. However, a disaster may cause correlated failures for the assigned resources, affecting the network reliability. This article focuses on determining the optimal resource assignment with maximal network reliability for SFNs. To solve the problem, this study proposes a hybrid algorithm integrating the genetic algorithm and tabu search to determine the optimal assignment, called the hybrid GA-TS algorithm (HGTA), and integrates minimal paths, recursive sum of disjoint products and the correlated binomial distribution to calculate network reliability. Several practical numerical experiments are adopted to demonstrate that HGTA has better computational quality than several popular soft computing algorithms.

Diversified models for portfolio selection based on uncertain semivariance

NASA Astrophysics Data System (ADS)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

NASA Astrophysics Data System (ADS)

Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

2017-12-01

Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

Weather prediction using a genetic memory

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

PubMed

Ramadan Suleiman, Ahmed; Nehdi, Moncef L

2017-02-07

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

NASA Astrophysics Data System (ADS)

Mirabi, Mohammad; Fatemi Ghomi, S. M. T.; Jolai, F.

2014-04-01

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper develops a novel hybrid genetic algorithm (HGA) with three genetic operators. Proposed HGA applies a modified approach to generate a pool of initial solutions, and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. We consider the make-to-order production approach that some sequences between jobs are assumed as tabu based on maximum allowable setup cost. In addition, the results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of solution.

A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

NASA Astrophysics Data System (ADS)

Jolai, Fariborz; Assadipour, Ghazal

Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.

Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms.

PubMed

Abedini, Mohammad; Moradi, Mohammad H; Hosseinian, S M

2016-03-01

This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.

PubMed

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem

PubMed Central

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. PMID:26167171

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

PubMed

Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

PubMed Central

Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software. PMID:28263994

Hybrid Nested Partitions and Math Programming Framework for Large-scale Combinatorial Optimization

DTIC Science & Technology

2010-03-31

optimization problems: 1) exact algorithms and 2) metaheuristic algorithms . This project will integrate concepts from these two technologies to develop...optimal solutions within an acceptable amount of computation time, and 2) metaheuristic algorithms such as genetic algorithms , tabu search, and the...integer programming decomposition approaches, such as Dantzig Wolfe decomposition and Lagrangian relaxation, and metaheuristics such as the Nested

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

PubMed Central

Kavitha, Muthu Subash; Ganesh Kumar, Pugalendhi; Park, Soon-Yong; Huh, Kyung-Hoe; Heo, Min-Suk; Kurita, Takio; Asano, Akira; An, Seo-Yong

2016-01-01

Objectives: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. Methods: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. Results: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942–0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. Conclusions: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage. PMID:27186991

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

PubMed Central

Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology. PMID:27403153

Hybrid genetic algorithm with an adaptive penalty function for fitting multimodal experimental data: application to exchange-coupled non-Kramers binuclear iron active sites.

PubMed

Beaser, Eric; Schwartz, Jennifer K; Bell, Caleb B; Solomon, Edward I

2011-09-26

A Genetic Algorithm (GA) is a stochastic optimization technique based on the mechanisms of biological evolution. These algorithms have been successfully applied in many fields to solve a variety of complex nonlinear problems. While they have been used with some success in chemical problems such as fitting spectroscopic and kinetic data, many have avoided their use due to the unconstrained nature of the fitting process. In engineering, this problem is now being addressed through incorporation of adaptive penalty functions, but their transfer to other fields has been slow. This study updates the Nanakorrn Adaptive Penalty function theory, expanding its validity beyond maximization problems to minimization as well. The expanded theory, using a hybrid genetic algorithm with an adaptive penalty function, was applied to analyze variable temperature variable field magnetic circular dichroism (VTVH MCD) spectroscopic data collected on exchange coupled Fe(II)Fe(II) enzyme active sites. The data obtained are described by a complex nonlinear multimodal solution space with at least 6 to 13 interdependent variables and are costly to search efficiently. The use of the hybrid GA is shown to improve the probability of detecting the global optimum. It also provides large gains in computational and user efficiency. This method allows a full search of a multimodal solution space, greatly improving the quality and confidence in the final solution obtained, and can be applied to other complex systems such as fitting of other spectroscopic or kinetics data.

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Lopez, Nicolas

This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm.

PubMed

Zhou, Xiuze; Lin, Fan; Yang, Lvqing; Nie, Jing; Tan, Qian; Zeng, Wenhua; Zhang, Nian

2016-01-01

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain).

PubMed

García Nieto, P J; Alonso Fernández, J R; de Cos Juez, F J; Sánchez Lasheras, F; Díaz Muñiz, C

2013-04-01

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. Copyright © 2013 Elsevier Inc. All rights reserved.

Multi-period project portfolio selection under risk considerations and stochastic income

NASA Astrophysics Data System (ADS)

Tofighian, Ali Asghar; Moezzi, Hamid; Khakzar Barfuei, Morteza; Shafiee, Mahmood

2018-02-01

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

New optimization model for routing and spectrum assignment with nodes insecurity

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-04-01

By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.

A New Approach for Solving the Generalized Traveling Salesman Problem

NASA Astrophysics Data System (ADS)

Pop, P. C.; Matei, O.; Sabo, C.

The generalized traveling problem (GTSP) is an extension of the classical traveling salesman problem. The GTSP is known to be an NP-hard problem and has many interesting applications. In this paper we present a local-global approach for the generalized traveling salesman problem. Based on this approach we describe a novel hybrid metaheuristic algorithm for solving the problem using genetic algorithms. Computational results are reported for Euclidean TSPlib instances and compared with the existing ones. The obtained results point out that our hybrid algorithm is an appropriate method to explore the search space of this complex problem and leads to good solutions in a reasonable amount of time.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

PubMed Central

Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend.

PubMed

Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Command and Control of Teams of Autonomous Units

DTIC Science & Technology

2012-06-01

done by a hybrid genetic algorithm (GA) particle swarm optimization ( PSO ) algorithm called PIDGION-alternate. This training algorithm is an ANN ...human controller will recognize the behaviors as being safe and correct. As the HyperNEAT approach produces Artificial Neural Nets ( ANN ), we can...optimization technique that generates efficient ANN controls from simple environmental feedback. FALCONET has been tested showing that it can produce

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua

2014-03-15

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented tomore » tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.« less

Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2015-12-01

The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.

Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series

NASA Astrophysics Data System (ADS)

McKinney, B. A.; Crowe, J. E., Jr.; Voss, H. U.; Crooke, P. S.; Barney, N.; Moore, J. H.

2006-02-01

We introduce a grammar-based hybrid approach to reverse engineering nonlinear ordinary differential equation models from observed time series. This hybrid approach combines a genetic algorithm to search the space of model architectures with a Kalman filter to estimate the model parameters. Domain-specific knowledge is used in a context-free grammar to restrict the search space for the functional form of the target model. We find that the hybrid approach outperforms a pure evolutionary algorithm method, and we observe features in the evolution of the dynamical models that correspond with the emergence of favorable model components. We apply the hybrid method to both artificially generated time series and experimentally observed protein levels from subjects who received the smallpox vaccine. From the observed data, we infer a cytokine protein interaction network for an individual’s response to the smallpox vaccine.

A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under e-supply chain environment.

PubMed

Li, Yanhui; Guo, Hao; Wang, Lin; Fu, Jing

2013-01-01

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

A hybrid artificial bee colony algorithm for numerical function optimization

NASA Astrophysics Data System (ADS)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

A sustainable genetic algorithm for satellite resource allocation

NASA Technical Reports Server (NTRS)

Abbott, R. J.; Campbell, M. L.; Krenz, W. C.

1995-01-01

A hybrid genetic algorithm is used to schedule tasks for 8 satellites, which can be modelled as a robot whose task is to retrieve objects from a two dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.

Study on optimal configuration of the grid-connected wind-solar-battery hybrid power system

NASA Astrophysics Data System (ADS)

Ma, Gang; Xu, Guchao; Ju, Rong; Wu, Tiantian

2017-08-01

The capacity allocation of each energy unit in the grid-connected wind-solar-battery hybrid power system is a significant segment in system design. In this paper, taking power grid dispatching into account, the research priorities are as follows: (1) We establish the mathematic models of each energy unit in the hybrid power system. (2) Based on dispatching of the power grid, energy surplus rate, system energy volatility and total cost, we establish the evaluation system for the wind-solar-battery power system and use a number of different devices as the constraint condition. (3) Based on an improved Genetic algorithm, we put forward a multi-objective optimisation algorithm to solve the optimal configuration problem in the hybrid power system, so we can achieve the high efficiency and economy of the grid-connected hybrid power system. The simulation result shows that the grid-connected wind-solar-battery hybrid power system has a higher comprehensive performance; the method of optimal configuration in this paper is useful and reasonable.

Comparison of optimization algorithms in intensity-modulated radiation therapy planning

NASA Astrophysics Data System (ADS)

Kendrick, Rachel

Intensity-modulated radiation therapy is used to better conform the radiation dose to the target, which includes avoiding healthy tissue. Planning programs employ optimization methods to search for the best fluence of each photon beam, and therefore to create the best treatment plan. The Computational Environment for Radiotherapy Research (CERR), a program written in MATLAB, was used to examine some commonly-used algorithms for one 5-beam plan. Algorithms include the genetic algorithm, quadratic programming, pattern search, constrained nonlinear optimization, simulated annealing, the optimization method used in Varian EclipseTM, and some hybrids of these. Quadratic programing, simulated annealing, and a quadratic/simulated annealing hybrid were also separately compared using different prescription doses. The results of each dose-volume histogram as well as the visual dose color wash were used to compare the plans. CERR's built-in quadratic programming provided the best overall plan, but avoidance of the organ-at-risk was rivaled by other programs. Hybrids of quadratic programming with some of these algorithms seems to suggest the possibility of better planning programs, as shown by the improved quadratic/simulated annealing plan when compared to the simulated annealing algorithm alone. Further experimentation will be done to improve cost functions and computational time.

Evolutionary Algorithms Approach to the Solution of Damage Detection Problems

NASA Astrophysics Data System (ADS)

Salazar Pinto, Pedro Yoajim; Begambre, Oscar

2010-09-01

In this work is proposed a new Self-Configured Hybrid Algorithm by combining the Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA). The aim of the proposed strategy is to increase the stability and accuracy of the search. The central idea is the concept of Guide Particle, this particle (the best PSO global in each generation) transmits its information to a particle of the following PSO generation, which is controlled by the GA. Thus, the proposed hybrid has an elitism feature that improves its performance and guarantees the convergence of the procedure. In different test carried out in benchmark functions, reported in the international literature, a better performance in stability and accuracy was observed; therefore the new algorithm was used to identify damage in a simple supported beam using modal data. Finally, it is worth noting that the algorithm is independent of the initial definition of heuristic parameters.

Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

NASA Astrophysics Data System (ADS)

Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

2017-02-01

In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

PubMed Central

Guo, Hao; Fu, Jing

2013-01-01

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment. PMID:24489489

Sensor Fusion, Prognostics, Diagnostics and Failure Mode Control for Complex Aerospace Systems

DTIC Science & Technology

2010-10-01

algorithm   and   to   then   tune   the   candidates   individually   using   known   metaheuristics .  As  will  be...parallel. The result of this arrangement is that the processing is a form that is analogous to standard parallel genetic algorithms , and as such...search algorithm then uses the hybrid of fitness data to rank the results. The ETRAS controller is developed using pre-selection, showing that a

Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms.

PubMed

Jaffe, Jacob D; Feeney, Caitlin M; Patel, Jinal; Lu, Xiaodong; Mani, D R

2016-11-01

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques. Graphical Abstract ᅟ.

Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Jaffe, Jacob D.; Feeney, Caitlin M.; Patel, Jinal; Lu, Xiaodong; Mani, D. R.

2016-11-01

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques.

Recourse-based facility-location problems in hybrid uncertain environment.

PubMed

Wang, Shuming; Watada, Junzo; Pedrycz, Witold

2010-08-01

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

Complex motion measurement using genetic algorithm

NASA Astrophysics Data System (ADS)

Shen, Jianjun; Tu, Dan; Shen, Zhenkang

1997-12-01

Genetic algorithm (GA) is an optimization technique that provides an untraditional approach to deal with many nonlinear, complicated problems. The notion of motion measurement using genetic algorithm arises from the fact that the motion measurement is virtually an optimization process based on some criterions. In the paper, we propose a complex motion measurement method using genetic algorithm based on block-matching criterion. The following three problems are mainly discussed and solved in the paper: (1) apply an adaptive method to modify the control parameters of GA that are critical to itself, and offer an elitism strategy at the same time (2) derive an evaluate function of motion measurement for GA based on block-matching technique (3) employ hill-climbing (HC) method hybridly to assist GA's search for the global optimal solution. Some other related problems are also discussed. At the end of paper, experiments result is listed. We employ six motion parameters for measurement in our experiments. Experiments result shows that the performance of our GA is good. The GA can find the object motion accurately and rapidly.

Accurate modeling of switched reluctance machine based on hybrid trained WNN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shoujun, E-mail: sunnyway@nwpu.edu.cn; Ge, Lefei; Ma, Shaojie

2014-04-15

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, themore » nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.« less

An effective hybrid self-adapting differential evolution algorithm for the joint replenishment and location-inventory problem in a three-level supply chain.

PubMed

Wang, Lin; Qu, Hui; Chen, Tao; Yan, Fang-Ping

2013-01-01

The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem.

An Effective Hybrid Self-Adapting Differential Evolution Algorithm for the Joint Replenishment and Location-Inventory Problem in a Three-Level Supply Chain

PubMed Central

Chen, Tao; Yan, Fang-Ping

2013-01-01

The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem. PMID:24453822

A Novel Algorithm Combining Finite State Method and Genetic Algorithm for Solving Crude Oil Scheduling Problem

PubMed Central

Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun

2014-01-01

A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

PubMed Central

Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

2015-01-01

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

PubMed Central

2015-01-01

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

Optimal Integration of Departures and Arrivals in Terminal Airspace

NASA Technical Reports Server (NTRS)

Xue, Min; Zelinski, Shannon Jean

2013-01-01

Coordination of operations with spatially and temporally shared resources, such as route segments, fixes, and runways, improves the efficiency of terminal airspace management. Problems in this category are, in general, computationally difficult compared to conventional scheduling problems. This paper presents a fast time algorithm formulation using a non-dominated sorting genetic algorithm (NSGA). It was first applied to a test problem introduced in existing literature. An experiment with a test problem showed that new methods can solve the 20 aircraft problem in fast time with a 65% or 440 second delay reduction using shared departure fixes. In order to test its application in a more realistic and complicated problem, the NSGA algorithm was applied to a problem in LAX terminal airspace, where interactions between 28% of LAX arrivals and 10% of LAX departures are resolved by spatial separation in current operations, which may introduce unnecessary delays. In this work, three types of separations - spatial, temporal, and hybrid separations - were formulated using the new algorithm. The hybrid separation combines both temporal and spatial separations. Results showed that although temporal separation achieved less delay than spatial separation with a small uncertainty buffer, spatial separation outperformed temporal separation when the uncertainty buffer was increased. Hybrid separation introduced much less delay than both spatial and temporal approaches. For a total of 15 interacting departures and arrivals, when compared to spatial separation, the delay reduction of hybrid separation varied between 11% or 3.1 minutes and 64% or 10.7 minutes corresponding to an uncertainty buffer from 0 to 60 seconds. Furthermore, as a comparison with the NSGA algorithm, a First-Come-First-Serve based heuristic method was implemented for the hybrid separation. Experiments showed that the results from the NSGA algorithm have 9% to 42% less delay than the heuristic method with varied uncertainty buffer sizes.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

NASA Astrophysics Data System (ADS)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

A master-slave parallel hybrid multi-objective evolutionary algorithm for groundwater remediation design under general hydrogeological conditions

NASA Astrophysics Data System (ADS)

Wu, J.; Yang, Y.; Luo, Q.; Wu, J.

2012-12-01

This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

PubMed Central

Barros, Allan Kardec; Ohnishi, Noboru

2016-01-01

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods. PMID:27891509

Multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kitagawa, Yosuke; Kitagawa, Koki; Nakamiya, Masaki; Kanazaki, Masahiro; Shimada, Toru

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

A hybrid genetic algorithm-queuing multi-compartment model for optimizing inpatient bed occupancy and associated costs.

PubMed

Belciug, Smaranda; Gorunescu, Florin

2016-03-01

Explore how efficient intelligent decision support systems, both easily understandable and straightforwardly implemented, can help modern hospital managers to optimize both bed occupancy and utilization costs. This paper proposes a hybrid genetic algorithm-queuing multi-compartment model for the patient flow in hospitals. A finite capacity queuing model with phase-type service distribution is combined with a compartmental model, and an associated cost model is set up. An evolutionary-based approach is used for enhancing the ability to optimize both bed management and associated costs. In addition, a "What-if analysis" shows how changing the model parameters could improve performance while controlling costs. The study uses bed-occupancy data collected at the Department of Geriatric Medicine - St. George's Hospital, London, period 1969-1984, and January 2000. The hybrid model revealed that a bed-occupancy exceeding 91%, implying a patient rejection rate around 1.1%, can be carried out with 159 beds plus 8 unstaffed beds. The same holding and penalty costs, but significantly different bed allocations (156 vs. 184 staffed beds, and 8 vs. 9 unstaffed beds, respectively) will result in significantly different costs (£755 vs. £1172). Moreover, once the arrival rate exceeds 7 patient/day, the costs associated to the finite capacity system become significantly smaller than those associated to an Erlang B queuing model (£134 vs. £947). Encoding the whole information provided by both the queuing system and the cost model through chromosomes, the genetic algorithm represents an efficient tool in optimizing the bed allocation and associated costs. The methodology can be extended to different medical departments with minor modifications in structure and parameterization. Copyright © 2016 Elsevier B.V. All rights reserved.

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

PubMed Central

2010-01-01

Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. Methods Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. Results We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. Conclusions We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait. PMID:20875103

Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

PubMed

Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

2016-01-01

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

PubMed

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering

PubMed Central

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed. PMID:25972896

Contrast improvement of continuous wave diffuse optical tomography reconstruction by hybrid approach using least square and genetic algorithm

NASA Astrophysics Data System (ADS)

Patra, Rusha; Dutta, Pranab K.

2015-07-01

Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10-3, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.

sp3-hybridized framework structure of group-14 elements discovered by genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, Manh Cuong; Zhao, Xin; Wang, Cai-Zhuang

2014-05-01

Group-14 elements, including C, Si, Ge, and Sn, can form various stable and metastable structures. Finding new metastable structures of group-14 elements with desirable physical properties for new technological applications has attracted a lot of interest. Using a genetic algorithm, we discovered a new low-energy metastable distorted sp3-hybridized framework structure of the group-14 elements. It has P42/mnm symmetry with 12 atoms per unit cell. The void volume of this structure is as large as 139.7Å3 for Si P42/mnm, and it can be used for gas or metal-atom encapsulation. Band-structure calculations show that P42/mnm structures of Si and Ge are semiconductingmore » with energy band gaps close to the optimal values for optoelectronic or photovoltaic applications. With metal-atom encapsulation, the P42/mnm structure would also be a candidate for rattling-mediated superconducting or used as thermoelectric materials.« less

Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm-artificial neural network method

NASA Astrophysics Data System (ADS)

Darvishvand, Leila; Kamkari, Babak; Kowsary, Farshad

2018-03-01

In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces. A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study. The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function. An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method. The trained ANN is used in conjunction with the GA to find the optimal design variables. The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method. It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces.

Stand-alone hybrid wind-photovoltaic power generation systems optimal sizing

NASA Astrophysics Data System (ADS)

Crǎciunescu, Aurelian; Popescu, Claudia; Popescu, Mihai; Florea, Leonard Marin

2013-10-01

Wind and photovoltaic energy resources have attracted energy sectors to generate power on a large scale. A drawback, common to these options, is their unpredictable nature and dependence on day time and meteorological conditions. Fortunately, the problems caused by the variable nature of these resources can be partially overcome by integrating the two resources in proper combination, using the strengths of one source to overcome the weakness of the other. The hybrid systems that combine wind and solar generating units with battery backup can attenuate their individual fluctuations and can match with the power requirements of the beneficiaries. In order to efficiently and economically utilize the hybrid energy system, one optimum match design sizing method is necessary. In this way, literature offers a variety of methods for multi-objective optimal designing of hybrid wind/photovoltaic (WG/PV) generating systems, one of the last being genetic algorithms (GA) and particle swarm optimization (PSO). In this paper, mathematical models of hybrid WG/PV components and a short description of the last proposed multi-objective optimization algorithms are given.

A novel approach for dimension reduction of microarray.

PubMed

Aziz, Rabia; Verma, C K; Srivastava, Namita

2017-12-01

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimising the production of succinate and lactate in Escherichia coli using a hybrid of artificial bee colony algorithm and minimisation of metabolic adjustment.

PubMed

Tang, Phooi Wah; Choon, Yee Wen; Mohamad, Mohd Saberi; Deris, Safaai; Napis, Suhaimi

2015-03-01

Metabolic engineering is a research field that focuses on the design of models for metabolism, and uses computational procedures to suggest genetic manipulation. It aims to improve the yield of particular chemical or biochemical products. Several traditional metabolic engineering methods are commonly used to increase the production of a desired target, but the products are always far below their theoretical maximums. Using numeral optimisation algorithms to identify gene knockouts may stall at a local minimum in a multivariable function. This paper proposes a hybrid of the artificial bee colony (ABC) algorithm and the minimisation of metabolic adjustment (MOMA) to predict an optimal set of solutions in order to optimise the production rate of succinate and lactate. The dataset used in this work was from the iJO1366 Escherichia coli metabolic network. The experimental results include the production rate, growth rate and a list of knockout genes. From the comparative analysis, ABCMOMA produced better results compared to previous works, showing potential for solving genetic engineering problems. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Swarm satellite mission scheduling & planning using Hybrid Dynamic Mutation Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Zixuan; Guo, Jian; Gill, Eberhard

2017-08-01

Space missions have traditionally been controlled by operators from a mission control center. Given the increasing number of satellites for some space missions, generating a command list for multiple satellites can be time-consuming and inefficient. Developing multi-satellite, onboard mission scheduling & planning techniques is, therefore, a key research field for future space mission operations. In this paper, an improved Genetic Algorithm (GA) using a new mutation strategy is proposed as a mission scheduling algorithm. This new mutation strategy, called Hybrid Dynamic Mutation (HDM), combines the advantages of both dynamic mutation strategy and adaptive mutation strategy, overcoming weaknesses such as early convergence and long computing time, which helps standard GA to be more efficient and accurate in dealing with complex missions. HDM-GA shows excellent performance in solving both unconstrained and constrained test functions. The experiments of using HDM-GA to simulate a multi-satellite, mission scheduling problem demonstrates that both the computation time and success rate mission requirements can be met. The results of a comparative test between HDM-GA and three other mutation strategies also show that HDM has outstanding performance in terms of speed and reliability.

High density DNA microarrays: algorithms and biomedical applications.

PubMed

Liu, Wei-Min

2004-08-01

DNA microarrays are devices capable of detecting the identity and abundance of numerous DNA or RNA segments in samples. They are used for analyzing gene expressions, identifying genetic markers and detecting mutations on a genomic scale. The fundamental chemical mechanism of DNA microarrays is the hybridization between probes and targets due to the hydrogen bonds of nucleotide base pairing. Since the cross hybridization is inevitable, and probes or targets may form undesirable secondary or tertiary structures, the microarray data contain noise and depend on experimental conditions. It is crucial to apply proper statistical algorithms to obtain useful signals from noisy data. After we obtained the signals of a large amount of probes, we need to derive the biomedical information such as the existence of a transcript in a cell, the difference of expression levels of a gene in multiple samples, and the type of a genetic marker. Furthermore, after the expression levels of thousands of genes or the genotypes of thousands of single nucleotide polymorphisms are determined, it is usually important to find a small number of genes or markers that are related to a disease, individual reactions to drugs, or other phenotypes. All these applications need careful data analyses and reliable algorithms.

Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design

USGS Publications Warehouse

Chiu, Yung-Chia; Nishikawa, Tracy; Martin, Peter

2012-01-01

Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctiveuse strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources.

Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Y.; Edwards, R.M.; Lee, K.Y.

1997-03-01

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against some realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (FBC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances ormore » uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrade.« less

A new optimized GA-RBF neural network algorithm.

PubMed

Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

Threshold matrix for digital halftoning by genetic algorithm optimization

NASA Astrophysics Data System (ADS)

Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero

1998-10-01

Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.

Evolutionary computation applied to the reconstruction of 3-D surface topography in the SEM.

PubMed

Kodama, Tetsuji; Li, Xiaoyuan; Nakahira, Kenji; Ito, Dai

2005-10-01

A genetic algorithm has been applied to the line profile reconstruction from the signals of the standard secondary electron (SE) and/or backscattered electron detectors in a scanning electron microscope. This method solves the topographical surface reconstruction problem as one of combinatorial optimization. To extend this optimization approach for three-dimensional (3-D) surface topography, this paper considers the use of a string coding where a 3-D surface topography is represented by a set of coordinates of vertices. We introduce the Delaunay triangulation, which attains the minimum roughness for any set of height data to capture the fundamental features of the surface being probed by an electron beam. With this coding, the strings are processed with a class of hybrid optimization algorithms that combine genetic algorithms and simulated annealing algorithms. Experimental results on SE images are presented.

A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling.

PubMed

Li, Bin-Bin; Wang, Ling

2007-06-01

This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for the multiobjective flow shop scheduling problem (FSSP), which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. On the one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in the discrete 0-1 hyperspace by using the updating operator of quantum gate and genetic operators of Q-bit. Moreover, random-key representation is used to convert the Q-bit representation to job permutation for evaluating the objective values of the schedule solution. On the other hand, permutation-based GA (PGA) is applied for both performing exploration in permutation-based scheduling space and stressing exploitation for good schedule solutions. To evaluate solutions in multiobjective sense, a randomly weighted linear-sum function is used in QGA, and a nondominated sorting technique including classification of Pareto fronts and fitness assignment is applied in PGA with regard to both proximity and diversity of solutions. To maintain the diversity of the population, two trimming techniques for population are proposed. The proposed HQGA is tested based on some multiobjective FSSPs. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed HQGA.

Disentangling the role of hybridization in the evolution of the endangered Arizona cliffrose (Purshia subintegra; Rosaceae): A molecular and morphological analysis

USGS Publications Warehouse

Travis, S.E.; Baggs, J.E.; Maschinski, J.

2008-01-01

Hybridization may threaten the conservation status of rare species through genetic assimilation and may confound the ability to distinguish among taxa. We studied these issues in an endangered shrub, Purshia subintegra (Rosaceae), known from four populations growing on limestone outcrops in central Arizona (USA). Using amplified fragment length polymorphisms (AFLP) and the Bayesian clustering algorithm implemented in STRUCTURE, we identified three distinct genetic lineages among Arizona Purshia subintegra and P. stansburiana. An initial split divided San Carlos Basin P. subintegra (considered P. pinkavae by Schaack) from northern P. stansburiana populations (FST = 0.394). A subsequent split separated northern P. stansburiana from two P. subintegra populations at Horseshoe Lake and Burro Creek (FST = 0.207), which comprised a nearly perfect admixture of the two lineages identified in the initial analysis. In the Verde River Valley P. subintegra is sympatric with P. stansburiana and exhibited an average 27% P. stansburiana genes for 5 of 6 stands analyzed, indicating ongoing hybridization and backcrossing with P. subintegra. Individuals carrying >90% P. subintegra markers are identifiable 68% of the time based on morphology, with leaf lobing, leaf size, and leaf length acting as the most reliable indicators of taxonomic status. However, the genetic and morphological distance correlation among individuals was low (r = 0.17, P = 0.0002), indicating that morphology cannot always accurately predict genetic admixture or taxonomy. Overall, our study confirmed the genetic distinctiveness of the San Carlos Basin population, an ancient natural hybrid origin of P. subintegra, and the presence of a hybrid swarm in the Verde Valley, whose conservation value may lie in its heightened genetic diversity. ?? 2007 Springer Science+Business Media B.V.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Partial discharge localization in power transformers based on the sequential quadratic programming-genetic algorithm adopting acoustic emission techniques

NASA Astrophysics Data System (ADS)

Liu, Hua-Long; Liu, Hua-Dong

2014-10-01

Partial discharge (PD) in power transformers is one of the prime reasons resulting in insulation degradation and power faults. Hence, it is of great importance to study the techniques of the detection and localization of PD in theory and practice. The detection and localization of PD employing acoustic emission (AE) techniques, as a kind of non-destructive testing, plus due to the advantages of powerful capability of locating and high precision, have been paid more and more attention. The localization algorithm is the key factor to decide the localization accuracy in AE localization of PD. Many kinds of localization algorithms exist for the PD source localization adopting AE techniques including intelligent and non-intelligent algorithms. However, the existed algorithms possess some defects such as the premature convergence phenomenon, poor local optimization ability and unsuitability for the field applications. To overcome the poor local optimization ability and easily caused premature convergence phenomenon of the fundamental genetic algorithm (GA), a new kind of improved GA is proposed, namely the sequence quadratic programming-genetic algorithm (SQP-GA). For the hybrid optimization algorithm, SQP-GA, the sequence quadratic programming (SQP) algorithm which is used as a basic operator is integrated into the fundamental GA, so the local searching ability of the fundamental GA is improved effectively and the premature convergence phenomenon is overcome. Experimental results of the numerical simulations of benchmark functions show that the hybrid optimization algorithm, SQP-GA, is better than the fundamental GA in the convergence speed and optimization precision, and the proposed algorithm in this paper has outstanding optimization effect. At the same time, the presented SQP-GA in the paper is applied to solve the ultrasonic localization problem of PD in transformers, then the ultrasonic localization method of PD in transformers based on the SQP-GA is proposed. And localization results based on the SQP-GA are compared with some algorithms such as the GA, some other intelligent and non-intelligent algorithms. The results of calculating examples both stimulated and spot experiments demonstrate that the localization method based on the SQP-GA can effectively prevent the results from getting trapped into the local optimum values, and the localization method is of great feasibility and very suitable for the field applications, and the precision of localization is enhanced, and the effectiveness of localization is ideal and satisfactory.

Design of 240,000 orthogonal 25mer DNA barcode probes.

PubMed

Xu, Qikai; Schlabach, Michael R; Hannon, Gregory J; Elledge, Stephen J

2009-02-17

DNA barcodes linked to genetic features greatly facilitate screening these features in pooled formats using microarray hybridization, and new tools are needed to design large sets of barcodes to allow construction of large barcoded mammalian libraries such as shRNA libraries. Here we report a framework for designing large sets of orthogonal barcode probes. We demonstrate the utility of this framework by designing 240,000 barcode probes and testing their performance by hybridization. From the test hybridizations, we also discovered new probe design rules that significantly reduce cross-hybridization after their introduction into the framework of the algorithm. These rules should improve the performance of DNA microarray probe designs for many applications.

Design of 240,000 orthogonal 25mer DNA barcode probes

PubMed Central

Xu, Qikai; Schlabach, Michael R.; Hannon, Gregory J.; Elledge, Stephen J.

2009-01-01

DNA barcodes linked to genetic features greatly facilitate screening these features in pooled formats using microarray hybridization, and new tools are needed to design large sets of barcodes to allow construction of large barcoded mammalian libraries such as shRNA libraries. Here we report a framework for designing large sets of orthogonal barcode probes. We demonstrate the utility of this framework by designing 240,000 barcode probes and testing their performance by hybridization. From the test hybridizations, we also discovered new probe design rules that significantly reduce cross-hybridization after their introduction into the framework of the algorithm. These rules should improve the performance of DNA microarray probe designs for many applications. PMID:19171886

An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

PubMed Central

Feng, Yanhong; Jia, Ke; He, Yichao

2014-01-01

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

2015-01-01

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Spatial targeting of agri-environmental policy using bilevel evolutionary optimization

USDA-ARS?s Scientific Manuscript database

In this study we describe the optimal designation of agri-environmental policy as a bilevel optimization problem and propose an integrated solution method using a hybrid genetic algorithm. The problem is characterized by a single leader, the agency, that establishes a policy with the goal of optimiz...

Building of Reusable Reverse Logistics Model and its Optimization Considering the Decision of Backorder or Next Arrival of Goods

NASA Astrophysics Data System (ADS)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu; Lee, Hee-Hyol

This paper deals with the building of the reusable reverse logistics model considering the decision of the backorder or the next arrival of goods. The optimization method to minimize the transportation cost and to minimize the volume of the backorder or the next arrival of goods occurred by the Just in Time delivery of the final delivery stage between the manufacturer and the processing center is proposed. Through the optimization algorithms using the priority-based genetic algorithm and the hybrid genetic algorithm, the sub-optimal delivery routes are determined. Based on the case study of a distilling and sale company in Busan in Korea, the new model of the reusable reverse logistics of empty bottles is built and the effectiveness of the proposed method is verified.

Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem

DOE PAGES

Stefanescu, Razvan; Schmidt, Kathleen; Hite, Jason; ...

2016-12-12

In this paper, we propose several algorithms to recover the location and intensity of a radiation source located in a simulated 250 × 180 m block of an urban center based on synthetic measurements. Radioactive decay and detection are Poisson random processes, so we employ likelihood functions based on this distribution. Owing to the domain geometry and the proposed response model, the negative logarithm of the likelihood is only piecewise continuous differentiable, and it has multiple local minima. To address these difficulties, we investigate three hybrid algorithms composed of mixed optimization techniques. For global optimization, we consider simulated annealing, particlemore » swarm, and genetic algorithm, which rely solely on objective function evaluations; that is, they do not evaluate the gradient in the objective function. By employing early stopping criteria for the global optimization methods, a pseudo-optimum point is obtained. This is subsequently utilized as the initial value by the deterministic implicit filtering method, which is able to find local extrema in non-smooth functions, to finish the search in a narrow domain. These new hybrid techniques, combining global optimization and implicit filtering address, difficulties associated with the non-smooth response, and their performances, are shown to significantly decrease the computational time over the global optimization methods. To quantify uncertainties associated with the source location and intensity, we employ the delayed rejection adaptive Metropolis and DiffeRential Evolution Adaptive Metropolis algorithms. Finally, marginal densities of the source properties are obtained, and the means of the chains compare accurately with the estimates produced by the hybrid algorithms.« less

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

NASA Astrophysics Data System (ADS)

Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid

2017-10-01

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

Hybrid Stochastic Search Technique based Suboptimal AGC Regulator Design for Power System using Constrained Feedback Control Strategy

NASA Astrophysics Data System (ADS)

Ibraheem, Omveer, Hasan, N.

2010-10-01

A new hybrid stochastic search technique is proposed to design of suboptimal AGC regulator for a two area interconnected non reheat thermal power system incorporating DC link in parallel with AC tie-line. In this technique, we are proposing the hybrid form of Genetic Algorithm (GA) and simulated annealing (SA) based regulator. GASA has been successfully applied to constrained feedback control problems where other PI based techniques have often failed. The main idea in this scheme is to seek a feasible PI based suboptimal solution at each sampling time. The feasible solution decreases the cost function rather than minimizing the cost function.

An algorithmic framework for multiobjective optimization.

PubMed

Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

An Algorithmic Framework for Multiobjective Optimization

PubMed Central

Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu

2013-07-01

Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Load Frequency Control of a Two-Area Thermal-Hybrid Power System Using a Novel Quasi-Opposition Harmony Search Algorithm

NASA Astrophysics Data System (ADS)

Mahto, Tarkeshwar; Mukherjee, V.

2016-09-01

In the present work, a two-area thermal-hybrid interconnected power system, consisting of a thermal unit in one area and a hybrid wind-diesel unit in other area is considered. Capacitive energy storage (CES) and CES with static synchronous series compensator (SSSC) are connected to the studied two-area model to compensate for varying load demand, intermittent output power and area frequency oscillation. A novel quasi-opposition harmony search (QOHS) algorithm is proposed and applied to tune the various tunable parameters of the studied power system model. Simulation study reveals that inclusion of CES unit in both the areas yields superb damping performance for frequency and tie-line power deviation. From the simulation results it is further revealed that inclusion of SSSC is not viable from both technical as well as economical point of view as no considerable improvement in transient performance is noted with its inclusion in the tie-line of the studied power system model. The results presented in this paper demonstrate the potential of the proposed QOHS algorithm and show its effectiveness and robustness for solving frequency and power drift problems of the studied power systems. Binary coded genetic algorithm is taken for sake of comparison.

Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor.

PubMed

Ruan, Jujun; Zhang, Chao; Li, Ya; Li, Peiyi; Yang, Zaizhi; Chen, Xiaohong; Huang, Mingzhi; Zhang, Tao

2017-02-01

This work proposes an on-line hybrid intelligent control system based on a genetic algorithm (GA) evolving fuzzy wavelet neural network software sensor to control dissolved oxygen (DO) in an anaerobic/anoxic/oxic process for treating papermaking wastewater. With the self-learning and memory abilities of neural network, handling the uncertainty capacity of fuzzy logic, analyzing local detail superiority of wavelet transform and global search of GA, this proposed control system can extract the dynamic behavior and complex interrelationships between various operation variables. The results indicate that the reasonable forecasting and control performances were achieved with optimal DO, and the effluent quality was stable at and below the desired values in real time. Our proposed hybrid approach proved to be a robust and effective DO control tool, attaining not only adequate effluent quality but also minimizing the demand for energy, and is easily integrated into a global monitoring system for purposes of cost management. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm.

PubMed

Arabasadi, Zeinab; Alizadehsani, Roohallah; Roshanzamir, Mohamad; Moosaei, Hossein; Yarifard, Ali Asghar

2017-04-01

Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset. Copyright © 2017 Elsevier B.V. All rights reserved.

Hybrid Genetic Algorithm - Local Search Method for Ground-Water Management

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.; Martin, P.

2008-12-01

Ground-water management problems commonly are formulated as a mixed-integer, non-linear programming problem (MINLP). Relying only on conventional gradient-search methods to solve the management problem is computationally fast; however, the methods may become trapped in a local optimum. Global-optimization schemes can identify the global optimum, but the convergence is very slow when the optimal solution approaches the global optimum. In this study, we developed a hybrid optimization scheme, which includes a genetic algorithm and a gradient-search method, to solve the MINLP. The genetic algorithm identifies a near- optimal solution, and the gradient search uses the near optimum to identify the global optimum. Our methodology is applied to a conjunctive-use project in the Warren ground-water basin, California. Hi- Desert Water District (HDWD), the primary water-manager in the basin, plans to construct a wastewater treatment plant to reduce future septic-tank effluent from reaching the ground-water system. The treated wastewater instead will recharge the ground-water basin via percolation ponds as part of a larger conjunctive-use strategy, subject to State regulations (e.g. minimum distances and travel times). HDWD wishes to identify the least-cost conjunctive-use strategies that control ground-water levels, meet regulations, and identify new production-well locations. As formulated, the MINLP objective is to minimize water-delivery costs subject to constraints including pump capacities, available recharge water, water-supply demand, water-level constraints, and potential new-well locations. The methodology was demonstrated by an enumerative search of the entire feasible solution and comparing the optimum solution with results from the branch-and-bound algorithm. The results also indicate that the hybrid method identifies the global optimum within an affordable computation time. Sensitivity analyses, which include testing different recharge-rate scenarios, pond layouts, and water-supply constraints, indicate that the number of new wells is insensitive to water-supply constraints; however, pumping rates and patterns of the existing wells are sensitive. The locations of new wells are mildly sensitive to the pond layout.

Wind speed time series reconstruction using a hybrid neural genetic approach

NASA Astrophysics Data System (ADS)

Rodriguez, H.; Flores, J. J.; Puig, V.; Morales, L.; Guerra, A.; Calderon, F.

2017-11-01

Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternatives. However, the wind speed is not constant, making the planning and operation at electric power systems a difficult activity. Knowing in advance the amount of raw material (wind speed) used for energy production allows us to estimate the energy to be generated by the power plant, helping the maintenance planning, the operational management, optimal operational cost. For these reasons, the forecast of wind speed becomes a necessary task. The forecast process involves the use of past observations from the variable to forecast (wind speed). To measure wind speed, weather stations use devices called anemometers, but due to poor maintenance, connection error, or natural wear, they may present false or missing data. In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series. The proposed methodology reconstructs the time series using a ANN defined by a Compact Genetic Algorithm.

Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

PubMed Central

Molla-Alizadeh-Zavardehi, S.; Tavakkoli-Moghaddam, R.; Lotfi, F. Hosseinzadeh

2014-01-01

This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms. PMID:24883359

Optimization of cutting parameters for machining time in turning process

NASA Astrophysics Data System (ADS)

Mavliutov, A. R.; Zlotnikov, E. G.

2018-03-01

This paper describes the most effective methods for nonlinear constraint optimization of cutting parameters in the turning process. Among them are Linearization Programming Method with Dual-Simplex algorithm, Interior Point method, and Augmented Lagrangian Genetic Algorithm (ALGA). Every each of them is tested on an actual example – the minimization of production rate in turning process. The computation was conducted in the MATLAB environment. The comparative results obtained from the application of these methods show: The optimal value of the linearized objective and the original function are the same. ALGA gives sufficiently accurate values, however, when the algorithm uses the Hybrid function with Interior Point algorithm, the resulted values have the maximal accuracy.

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

PubMed

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

NASA Astrophysics Data System (ADS)

Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

2016-03-01

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation

NASA Astrophysics Data System (ADS)

MacNish, Cara

2007-12-01

Randomised population-based algorithms, such as evolutionary, genetic and swarm-based algorithms, and their hybrids with traditional search techniques, have proven successful and robust on many difficult real-valued optimisation problems. This success, along with the readily applicable nature of these techniques, has led to an explosion in the number of algorithms and variants proposed. In order for the field to advance it is necessary to carry out effective comparative evaluations of these algorithms, and thereby better identify and understand those properties that lead to better performance. This paper discusses the difficulties of providing benchmarking of evolutionary and allied algorithms that is both meaningful and logistically viable. To be meaningful the benchmarking test must give a fair comparison that is free, as far as possible, from biases that favour one style of algorithm over another. To be logistically viable it must overcome the need for pairwise comparison between all the proposed algorithms. To address the first problem, we begin by attempting to identify the biases that are inherent in commonly used benchmarking functions. We then describe a suite of test problems, generated recursively as self-similar or fractal landscapes, designed to overcome these biases. For the second, we describe a server that uses web services to allow researchers to 'plug in' their algorithms, running on their local machines, to a central benchmarking repository.

CI Controls for Energy and Environment

NASA Technical Reports Server (NTRS)

Biondo, Samuel J.

1996-01-01

Computational intelligence (CI) is a rapidly evolving field that utilizes life imitating metaphors for guiding model building including, but not limited to neural networks, fuzzy logic, genetic algorithms, artificial life, and hybrid CI paradigms. Although the boundaries between artificial intelligence (AI) and CI are not distinct, their research communities are separate and distinct. CI researchers tend to focus on processing numerical data from sensors, while the AI community generally relies on symbolic computing to capture human knowledge. In both areas, there is a great deal of interest and activity in hybrid systems that can offset the limitations of individual methods, extend their capabilities, and create new capabilities. Examples of the benefits that can accrue from hybrid systems are contained.

Hybrid Architectures and Their Impact on Intelligent Design

NASA Technical Reports Server (NTRS)

Kandel, Abe

1996-01-01

In this presentation we investigate a novel framework for the design of autonomous fuzzy intelligent systems. The system integrates the following modules into a single autonomous entity: (1) a fuzzy expert system; (2) artificial neural network; (3) genetic algorithm; and (4) case-base reasoning. We describe the integration of these units into one intelligent structure and discuss potential applications.

Using genetic algorithm to solve a new multi-period stochastic optimization model

NASA Astrophysics Data System (ADS)

Zhang, Xin-Li; Zhang, Ke-Cun

2009-09-01

This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.

Wear rate optimization of Al/SiCnp/e-glass fibre hybrid metal matrix composites using Taguchi method and genetic algorithm and development of wear model using artificial neural networks

NASA Astrophysics Data System (ADS)

Bongale, Arunkumar M.; Kumar, Satish; Sachit, T. S.; Jadhav, Priya

2018-03-01

Studies on wear properties of Aluminium based hybrid nano composite materials, processed through powder metallurgy technique, are reported in the present study. Silicon Carbide nano particles and E-glass fibre are reinforced in pure aluminium matrix to fabricate hybrid nano composite material samples. Pin-on-Disc wear testing equipment is used to evaluate dry sliding wear properties of the composite samples. The tests were conducted following the Taguchi’s Design of Experiments method. Signal-to-Noise ratio analysis and Analysis of Variance are carried out on the test data to find out the influence of test parameters on the wear rate. Scanning Electron Microscopic analysis and Energy Dispersive x-ray analysis are conducted on the worn surfaces to find out the wear mechanisms responsible for wear of the composites. Multiple linear regression analysis and Genetic Algorithm techniques are employed for optimization of wear test parameters to yield minimum wear of the composite samples. Finally, a wear model is built by the application of Artificial Neural Networks to predict the wear rate of the composite material, under different testing conditions. The predicted values of wear rate are found to be very close to the experimental values with a deviation in the range of 0.15% to 8.09%.

Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.

PubMed

Ullah, Azmat; Malik, Suheel Abdullah; Alimgeer, Khurram Saleem

2018-01-01

In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation.

PubMed

Cheema, Jitender Jit Singh; Sankpal, Narendra V; Tambe, Sanjeev S; Kulkarni, Bhaskar D

2002-01-01

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.

A hybrid meta-heuristic algorithm for the vehicle routing problem with stochastic travel times considering the driver's satisfaction

NASA Astrophysics Data System (ADS)

Tavakkoli-Moghaddam, Reza; Alinaghian, Mehdi; Salamat-Bakhsh, Alireza; Norouzi, Narges

2012-05-01

A vehicle routing problem is a significant problem that has attracted great attention from researchers in recent years. The main objectives of the vehicle routing problem are to minimize the traveled distance, total traveling time, number of vehicles and cost function of transportation. Reducing these variables leads to decreasing the total cost and increasing the driver's satisfaction level. On the other hand, this satisfaction, which will decrease by increasing the service time, is considered as an important logistic problem for a company. The stochastic time dominated by a probability variable leads to variation of the service time, while it is ignored in classical routing problems. This paper investigates the problem of the increasing service time by using the stochastic time for each tour such that the total traveling time of the vehicles is limited to a specific limit based on a defined probability. Since exact solutions of the vehicle routing problem that belong to the category of NP-hard problems are not practical in a large scale, a hybrid algorithm based on simulated annealing with genetic operators was proposed to obtain an efficient solution with reasonable computational cost and time. Finally, for some small cases, the related results of the proposed algorithm were compared with results obtained by the Lingo 8 software. The obtained results indicate the efficiency of the proposed hybrid simulated annealing algorithm.

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.

PubMed

Biswas, Surama; Dutta, Subarna; Acharyya, Sriyankar

2017-12-01

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

An Optimization Study of Hot Stamping Operation

NASA Astrophysics Data System (ADS)

Ghoo, Bonyoung; Umezu, Yasuyoshi; Watanabe, Yuko; Ma, Ninshu; Averill, Ron

2010-06-01

In the present study, 3-dimensional finite element analyses for hot-stamping processes of Audi B-pillar product are conducted using JSTAMP/NV and HEEDS. Special attention is paid to the optimization of simulation technology coupling with thermal-mechanical formulations. Numerical simulation based on FEM technology and optimization design using the hybrid adaptive SHERPA algorithm are applied to hot stamping operation to improve productivity. The robustness of the SHERPA algorithm is found through the results of the benchmark example. The SHERPA algorithm is shown to be far superior to the GA (Genetic Algorithm) in terms of efficiency, whose calculation time is about 7 times faster than that of the GA. The SHERPA algorithm could show high performance in a large scale problem having complicated design space and long calculation time.

A hybrid approach for efficient anomaly detection using metaheuristic methods

PubMed Central

Ghanem, Tamer F.; Elkilani, Wail S.; Abdul-kader, Hatem M.

2014-01-01

Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms. PMID:26199752

A hybrid approach for efficient anomaly detection using metaheuristic methods.

PubMed

Ghanem, Tamer F; Elkilani, Wail S; Abdul-Kader, Hatem M

2015-07-01

Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Optimal design of the first stage of the plate-fin heat exchanger for the EAST cryogenic system

NASA Astrophysics Data System (ADS)

Qingfeng, JIANG; Zhigang, ZHU; Qiyong, ZHANG; Ming, ZHUANG; Xiaofei, LU

2018-03-01

The size of the heat exchanger is an important factor determining the dimensions of the cold box in helium cryogenic systems. In this paper, a counter-flow multi-stream plate-fin heat exchanger is optimized by means of a spatial interpolation method coupled with a hybrid genetic algorithm. Compared with empirical correlations, this spatial interpolation algorithm based on a kriging model can be adopted to more precisely predict the Colburn heat transfer factors and Fanning friction factors of offset-strip fins. Moreover, strict computational fluid dynamics simulations can be carried out to predict the heat transfer and friction performance in the absence of reliable experimental data. Within the constraints of heat exchange requirements, maximum allowable pressure drop, existing manufacturing techniques and structural strength, a mathematical model of an optimized design with discrete and continuous variables based on a hybrid genetic algorithm is established in order to minimize the volume. The results show that for the first-stage heat exchanger in the EAST refrigerator, the structural size could be decreased from the original 2.200 × 0.600 × 0.627 (m3) to the optimized 1.854 × 0.420 × 0.340 (m3), with a large reduction in volume. The current work demonstrates that the proposed method could be a useful tool to achieve optimization in an actual engineering project during the practical design process.

Hybrid cryptosystem RSA - CRT optimization and VMPC

NASA Astrophysics Data System (ADS)

Rahmadani, R.; Mawengkang, H.; Sutarman

2018-03-01

Hybrid cryptosystem combines symmetric algorithms and asymmetric algorithms. This combination utilizes speeds on encryption/decryption processes of symmetric algorithms and asymmetric algorithms to secure symmetric keys. In this paper we propose hybrid cryptosystem that combine symmetric algorithms VMPC and asymmetric algorithms RSA - CRT optimization. RSA - CRT optimization speeds up the decryption process by obtaining plaintext with dp and p key only, so there is no need to perform CRT processes. The VMPC algorithm is more efficient in software implementation and reduces known weaknesses in RC4 key generation. The results show hybrid cryptosystem RSA - CRT optimization and VMPC is faster than hybrid cryptosystem RSA - VMPC and hybrid cryptosystem RSA - CRT - VMPC. Keyword : Cryptography, RSA, RSA - CRT, VMPC, Hybrid Cryptosystem.

Genetic algorithms and their use in Geophysical Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parker, Paul B.

1999-04-01

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show thatmore » certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.« less

Genetic algorithms and their use in geophysical problems

NASA Astrophysics Data System (ADS)

Parker, Paul Bradley

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.

Optimal Design of Wind-PV-Diesel-Battery System using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Suryoatmojo, Heri; Hiyama, Takashi; Elbaset, Adel A.; Ashari, Mochamad

Application of diesel generators to supply the load demand on isolated islands in Indonesia has widely spread. With increases in oil price and the concerns about global warming, the integration of diesel generators with renewable energy systems have become an attractive energy sources for supplying the load demand. This paper performs an optimal design of integrated system involving Wind-PV-Diesel-Battery system for isolated island with CO2 emission evaluation by using genetic algorithm. The proposed system has been designed for the hybrid power generation in East Nusa Tenggara, Indonesia-latitude 09.30S, longitude 122.0E. From simulation results, the proposed system is able to minimize the total annual cost of the system under study and reduce CO2 emission generated by diesel generators.

Operating room scheduling using hybrid clustering priority rule and genetic algorithm

NASA Astrophysics Data System (ADS)

Santoso, Linda Wahyuni; Sinawan, Aisyah Ashrinawati; Wijaya, Andi Rahadiyan; Sudiarso, Andi; Masruroh, Nur Aini; Herliansyah, Muhammad Kusumawan

2017-11-01

Operating room is a bottleneck resource in most hospitals so that operating room scheduling system will influence the whole performance of the hospitals. This research develops a mathematical model of operating room scheduling for elective patients which considers patient priority with limit number of surgeons, operating rooms, and nurse team. Clustering analysis was conducted to the data of surgery durations using hierarchical and non-hierarchical methods. The priority rule of each resulting cluster was determined using Shortest Processing Time method. Genetic Algorithm was used to generate daily operating room schedule which resulted in the lowest values of patient waiting time and nurse overtime. The computational results show that this proposed model reduced patient waiting time by approximately 32.22% and nurse overtime by approximately 32.74% when compared to actual schedule.

Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm

PubMed Central

Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

2016-01-01

Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

PubMed Central

Ramadan Suleiman, Ahmed; Nehdi, Moncef L.

2017-01-01

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. PMID:28772495

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya

2015-10-01

The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.

Optimal design of damping layers in SMA/GFRP laminated hybrid composites

NASA Astrophysics Data System (ADS)

Haghdoust, P.; Cinquemani, S.; Lo Conte, A.; Lecis, N.

2017-10-01

This work describes the optimization of the shape profiles for shape memory alloys (SMA) sheets in hybrid layered composite structures, i.e. slender beams or thinner plates, designed for the passive attenuation of flexural vibrations. The paper starts with the description of the material and architecture of the investigated hybrid layered composite. An analytical method, for evaluating the energy dissipation inside a vibrating cantilever beam is developed. The analytical solution is then followed by a shape profile optimization of the inserts, using a genetic algorithm to minimize the SMA material layer usage, while maintaining target level of structural damping. Delamination problem at SMA/glass fiber reinforced polymer interface is discussed. At the end, the proposed methodology has been applied to study the hybridization of a wind turbine layered structure blade with SMA material, in order to increase its passive damping.

Cost- and reliability-oriented aggregation point association in long-term evolution and passive optical network hybrid access infrastructure for smart grid neighborhood area network

NASA Astrophysics Data System (ADS)

Cheng, Xiao; Feng, Lei; Zhou, Fanqin; Wei, Lei; Yu, Peng; Li, Wenjing

2018-02-01

With the rapid development of the smart grid, the data aggregation point (AP) in the neighborhood area network (NAN) is becoming increasingly important for forwarding the information between the home area network and wide area network. Due to limited budget, it is unable to use one-single access technology to meet the ongoing requirements on AP coverage. This paper first introduces the wired and wireless hybrid access network with the integration of long-term evolution (LTE) and passive optical network (PON) system for NAN, which allows a good trade-off among cost, flexibility, and reliability. Then, based on the already existing wireless LTE network, an AP association optimization model is proposed to make the PON serve as many APs as possible, considering both the economic efficiency and network reliability. Moreover, since the features of the constraints and variables of this NP-hard problem, a hybrid intelligent optimization algorithm is proposed, which is achieved by the mixture of the genetic, ant colony and dynamic greedy algorithm. By comparing with other published methods, simulation results verify the performance of the proposed method in improving the AP coverage and the performance of the proposed algorithm in terms of convergence.

Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

NASA Astrophysics Data System (ADS)

Du, Jiaoman; Yu, Lean; Li, Xiang

2016-04-01

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data.

PubMed

Tong, Dong Ling; Schierz, Amanda C

2011-09-01

Suitable techniques for microarray analysis have been widely researched, particularly for the study of marker genes expressed to a specific type of cancer. Most of the machine learning methods that have been applied to significant gene selection focus on the classification ability rather than the selection ability of the method. These methods also require the microarray data to be preprocessed before analysis takes place. The objective of this study is to develop a hybrid genetic algorithm-neural network (GANN) model that emphasises feature selection and can operate on unpreprocessed microarray data. The GANN is a hybrid model where the fitness value of the genetic algorithm (GA) is based upon the number of samples correctly labelled by a standard feedforward artificial neural network (ANN). The model is evaluated by using two benchmark microarray datasets with different array platforms and differing number of classes (a 2-class oligonucleotide microarray data for acute leukaemia and a 4-class complementary DNA (cDNA) microarray dataset for SRBCTs (small round blue cell tumours)). The underlying concept of the GANN algorithm is to select highly informative genes by co-evolving both the GA fitness function and the ANN weights at the same time. The novel GANN selected approximately 50% of the same genes as the original studies. This may indicate that these common genes are more biologically significant than other genes in the datasets. The remaining 50% of the significant genes identified were used to build predictive models and for both datasets, the models based on the set of genes extracted by the GANN method produced more accurate results. The results also suggest that the GANN method not only can detect genes that are exclusively associated with a single cancer type but can also explore the genes that are differentially expressed in multiple cancer types. The results show that the GANN model has successfully extracted statistically significant genes from the unpreprocessed microarray data as well as extracting known biologically significant genes. We also show that assessing the biological significance of genes based on classification accuracy may be misleading and though the GANN's set of extra genes prove to be more statistically significant than those selected by other methods, a biological assessment of these genes is highly recommended to confirm their functionality. Copyright © 2011 Elsevier B.V. All rights reserved.

A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

PubMed

Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein

2018-03-01

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.

Non-adaptive and adaptive hybrid approaches for enhancing water quality management

NASA Astrophysics Data System (ADS)

Kalwij, Ineke M.; Peralta, Richard C.

2008-09-01

SummaryUsing optimization to help solve groundwater management problems cost-effectively is becoming increasingly important. Hybrid optimization approaches, that combine two or more optimization algorithms, will become valuable and common tools for addressing complex nonlinear hydrologic problems. Hybrid heuristic optimizers have capabilities far beyond those of a simple genetic algorithm (SGA), and are continuously improving. SGAs having only parent selection, crossover, and mutation are inefficient and rarely used for optimizing contaminant transport management. Even an advanced genetic algorithm (AGA) that includes elitism (to emphasize using the best strategies as parents) and healing (to help assure optimal strategy feasibility) is undesirably inefficient. Much more efficient than an AGA is the presented hybrid (AGCT), which adds comprehensive tabu search (TS) features to an AGA. TS mechanisms (TS probability, tabu list size, search coarseness and solution space size, and a TS threshold value) force the optimizer to search portions of the solution space that yield superior pumping strategies, and to avoid reproducing similar or inferior strategies. An AGCT characteristic is that TS control parameters are unchanging during optimization. However, TS parameter values that are ideal for optimization commencement can be undesirable when nearing assumed global optimality. The second presented hybrid, termed global converger (GC), is significantly better than the AGCT. GC includes AGCT plus feedback-driven auto-adaptive control that dynamically changes TS parameters during run-time. Before comparing AGCT and GC, we empirically derived scaled dimensionless TS control parameter guidelines by evaluating 50 sets of parameter values for a hypothetical optimization problem. For the hypothetical area, AGCT optimized both well locations and pumping rates. The parameters are useful starting values because using trial-and-error to identify an ideal combination of control parameter values for a new optimization problem can be time consuming. For comparison, AGA, AGCT, and GC are applied to optimize pumping rates for assumed well locations of a complex large-scale contaminant transport and remediation optimization problem at Blaine Naval Ammunition Depot (NAD). Both hybrid approaches converged more closely to the optimal solution than the non-hybrid AGA. GC averaged 18.79% better convergence than AGCT, and 31.9% than AGA, within the same computation time (12.5 days). AGCT averaged 13.1% better convergence than AGA. The GC can significantly reduce the burden of employing computationally intensive hydrologic simulation models within a limited time period and for real-world optimization problems. Although demonstrated for a groundwater quality problem, it is also applicable to other arenas, such as managing salt water intrusion and surface water contaminant loading.

Study on Multi-stage Logistics System Design Problem with Inventory Considering Demand Change by Hybrid Genetic Algorithm

NASA Astrophysics Data System (ADS)

Inoue, Hisaki; Gen, Mitsuo

The logistics model used in this study is 3-stage model employed by an automobile company, which aims to solve traffic problems at a total minimum cost. Recently, research on the metaheuristics method has advanced as an approximate means for solving optimization problems like this model. These problems can be solved using various methods such as the genetic algorithm (GA), simulated annealing, and tabu search. GA is superior in robustness and adjustability toward a change in the structure of these problems. However, GA has a disadvantage in that it has a slightly inefficient search performance because it carries out a multi-point search. A hybrid GA that combines another method is attracting considerable attention since it can compensate for a fault to a partial solution that early convergence gives a bad influence on a result. In this study, we propose a novel hybrid random key-based GA(h-rkGA) that combines local search and parameter tuning of crossover rate and mutation rate; h-rkGA is an improved version of the random key-based GA (rk-GA). We attempted comparative experiments with spanning tree-based GA, priority based GA and random key-based GA. Further, we attempted comparative experiments with “h-GA by only local search” and “h-GA by only parameter tuning”. We reported the effectiveness of the proposed method on the basis of the results of these experiments.

GENIE: a hybrid genetic algorithm for feature classification in multispectral images

NASA Astrophysics Data System (ADS)

Perkins, Simon J.; Theiler, James P.; Brumby, Steven P.; Harvey, Neal R.; Porter, Reid B.; Szymanski, John J.; Bloch, Jeffrey J.

2000-10-01

We consider the problem of pixel-by-pixel classification of a multi- spectral image using supervised learning. Conventional spuervised classification techniques such as maximum likelihood classification and less conventional ones s uch as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why: the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or maybe the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.

Implementation of hybrid clustering based on partitioning around medoids algorithm and divisive analysis on human Papillomavirus DNA

NASA Astrophysics Data System (ADS)

Arimbi, Mentari Dian; Bustamam, Alhadi; Lestari, Dian

2017-03-01

Data clustering can be executed through partition or hierarchical method for many types of data including DNA sequences. Both clustering methods can be combined by processing partition algorithm in the first level and hierarchical in the second level, called hybrid clustering. In the partition phase some popular methods such as PAM, K-means, or Fuzzy c-means methods could be applied. In this study we selected partitioning around medoids (PAM) in our partition stage. Furthermore, following the partition algorithm, in hierarchical stage we applied divisive analysis algorithm (DIANA) in order to have more specific clusters and sub clusters structures. The number of main clusters is determined using Davies Bouldin Index (DBI) value. We choose the optimal number of clusters if the results minimize the DBI value. In this work, we conduct the clustering on 1252 HPV DNA sequences data from GenBank. The characteristic extraction is initially performed, followed by normalizing and genetic distance calculation using Euclidean distance. In our implementation, we used the hybrid PAM and DIANA using the R open source programming tool. In our results, we obtained 3 main clusters with average DBI value is 0.979, using PAM in the first stage. After executing DIANA in the second stage, we obtained 4 sub clusters for Cluster-1, 9 sub clusters for Cluster-2 and 2 sub clusters in Cluster-3, with the BDI value 0.972, 0.771, and 0.768 for each main cluster respectively. Since the second stage produce lower DBI value compare to the DBI value in the first stage, we conclude that this hybrid approach can improve the accuracy of our clustering results.

HYBRID FAST HANKEL TRANSFORM ALGORITHM FOR ELECTROMAGNETIC MODELING

EPA Science Inventory

A hybrid fast Hankel transform algorithm has been developed that uses several complementary features of two existing algorithms: Anderson's digital filtering or fast Hankel transform (FHT) algorithm and Chave's quadrature and continued fraction algorithm. A hybrid FHT subprogram ...

Optimal Integration of Departure and Arrivals in Terminal Airspace

NASA Technical Reports Server (NTRS)

Xue, Min; Zelinski, Shannon Jean

2012-01-01

Coordination of operations with spatially and temporally shared resources such as route segments, fixes, and runways improves the efficiency of terminal airspace management. Problems in this category include scheduling and routing, thus they are normally difficult to solve compared with pure scheduling problems. In order to reduce the computational time, a fast time algorithm formulation using a non-dominated sorting genetic algorithm (NSGA) was introduced in this work and applied to a test case based on existing literature. The experiment showed that new method can solve the whole problem in fast time instead of solving sub-problems sequentially with a window technique. The results showed a 60% or 406 second delay reduction was achieved by sharing departure fixes (more details on the comparison with MILP results will be presented in the final paper). Furthermore, the NSGA algorithm was applied to a problem in LAX terminal airspace, where interactions between 28% of LAX arrivals and 10% of LAX departures are resolved by spatial segregation, which may introduce unnecessary delays. In this work, spatial segregation, temporal segregation, and hybrid segregation were formulated using the new algorithm. Results showed that spatial and temporal segregation approaches achieved similar delay. Hybrid segregation introduced much less delay than the other two approaches. For a total of 9 interacting departures and arrivals, delay reduction varied from 4 minutes to 6.4 minutes corresponding flight time uncertainty from 0 to 60 seconds. Considering the amount of flights that could be affected, total annual savings with hybrid segregation would be significant.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

PubMed

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

PubMed Central

Salehi, Mojtaba

2010-01-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

Real-time energy-saving metro train rescheduling with primary delay identification

PubMed Central

Li, Keping; Schonfeld, Paul

2018-01-01

This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the schedule, with the delay identification results as input. Then, based on the infrastructure data of Beijing Subway Line 4 of China, case studies are presented to demonstrate the effectiveness and efficiency of the solution approach. The results show that the algorithm can quickly and accurately identify primary delays among different types of delays. The economic cost of energy consumption and total delay is considerably reduced (by more than 10% in each case). The computation time of the Hybrid-GA is low enough for rescheduling online. Sensitivity analyses further demonstrate that the proposed approach can be used as a decision-making support tool for operators. PMID:29474471

apGA: An adaptive parallel genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liepins, G.E.; Baluja, S.

1991-01-01

We develop apGA, a parallel variant of the standard generational GA, that combines aggressive search with perpetual novelty, yet is able to preserve enough genetic structure to optimally solve variably scaled, non-uniform block deceptive and hierarchical deceptive problems. apGA combines elitism, adaptive mutation, adaptive exponential scaling, and temporal memory. We present empirical results for six classes of problems, including the DeJong test suite. Although we have not investigated hybrids, we note that apGA could be incorporated into other recent GA variants such as GENITOR, CHC, and the recombination stage of mGA. 12 refs., 2 figs., 2 tabs.

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

NASA Astrophysics Data System (ADS)

Nouri, Houssem Eddine; Belkahla Driss, Olfa; Ghédira, Khaled

2018-03-01

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

An automatic scaling method for obtaining the trace and parameters from oblique ionogram based on hybrid genetic algorithm

NASA Astrophysics Data System (ADS)

Song, Huan; Hu, Yaogai; Jiang, Chunhua; Zhou, Chen; Zhao, Zhengyu; Zou, Xianjian

2016-12-01

Scaling oblique ionogram plays an important role in obtaining ionospheric structure at the midpoint of oblique sounding path. The paper proposed an automatic scaling method to extract the trace and parameters of oblique ionogram based on hybrid genetic algorithm (HGA). The extracted 10 parameters come from F2 layer and Es layer, such as maximum observation frequency, critical frequency, and virtual height. The method adopts quasi-parabolic (QP) model to describe F2 layer's electron density profile that is used to synthesize trace. And it utilizes secant theorem, Martyn's equivalent path theorem, image processing technology, and echoes' characteristics to determine seven parameters' best fit values, and three parameter's initial values in QP model to set up their searching spaces which are the needed input data of HGA. Then HGA searches the three parameters' best fit values from their searching spaces based on the fitness between the synthesized trace and the real trace. In order to verify the performance of the method, 240 oblique ionograms are scaled and their results are compared with manual scaling results and the inversion results of the corresponding vertical ionograms. The comparison results show that the scaling results are accurate or at least adequate 60-90% of the time.

Extended behavioural modelling of FET and lattice-mismatched HEMT devices

NASA Astrophysics Data System (ADS)

Khawam, Yahya; Albasha, Lutfi

2017-07-01

This study presents an improved large signal model that can be used for high electron mobility transistors (HEMTs) and field effect transistors using measurement-based behavioural modelling techniques. The steps for accurate large and small signal modelling for transistor are also discussed. The proposed DC model is based on the Fager model since it compensates between the number of model's parameters and accuracy. The objective is to increase the accuracy of the drain-source current model with respect to any change in gate or drain voltages. Also, the objective is to extend the improved DC model to account for soft breakdown and kink effect found in some variants of HEMT devices. A hybrid Newton's-Genetic algorithm is used in order to determine the unknown parameters in the developed model. In addition to accurate modelling of a transistor's DC characteristics, the complete large signal model is modelled using multi-bias s-parameter measurements. The way that the complete model is performed is by using a hybrid multi-objective optimisation technique (Non-dominated Sorting Genetic Algorithm II) and local minimum search (multivariable Newton's method) for parasitic elements extraction. Finally, the results of DC modelling and multi-bias s-parameters modelling are presented, and three-device modelling recommendations are discussed.

A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

NASA Astrophysics Data System (ADS)

Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.

2015-08-01

Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

PubMed

Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

2014-03-01

Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Hybrid time-frequency domain equalization based on sign-sign joint decision multimodulus algorithm for 6 × 6 mode division multiplexing system

NASA Astrophysics Data System (ADS)

Li, Jiao; Hu, Guijun; Gong, Caili; Li, Li

2018-02-01

In this paper, we propose a hybrid time-frequency domain sign-sign joint decision multimodulus algorithm (Hybrid-SJDMMA) for mode-demultiplexing in a 6 × 6 mode division multiplexing (MDM) system with high-order QAM modulation. The equalization performance of Hybrid-SJDMMA was evaluated and compared with the frequency domain multimodulus algorithm (FD-MMA) and the hybrid time-frequency domain sign-sign multimodulus algorithm (Hybrid-SMMA). Simulation results revealed that Hybrid-SJDMMA exhibits a significantly lower computational complexity than FD-MMA, and its convergence speed is similar to that of FD-MMA. Additionally, the bit-error-rate performance of Hybrid-SJDMMA was obviously better than FD-MMA and Hybrid-SMMA for 16 QAM and 64 QAM.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

PubMed

Hossain, Monowar; Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Horan, Ben; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

PubMed Central

Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M.; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations. PMID:29702645

Genetic basis to hybrid inviability is more complex than hybrid male sterility in Caenorhabditis nematodes.

PubMed

Bundus, Joanna D; Wang, Donglin; Cutter, Asher D

2018-04-07

Hybrid male sterility often evolves before female sterility or inviability of hybrids, implying that the accumulation of divergence between separated lineages should lead hybrid male sterility to have a more polygenic basis. However, experimental evidence is mixed. Here, we use the nematodes Caenorhabditis remanei and C. latens to characterize the underlying genetic basis of asymmetric hybrid male sterility and hybrid inviability. We demonstrate that hybrid male sterility is consistent with a simple genetic basis, involving a single X-autosome incompatibility. We also show that hybrid inviability involves more genomic compartments, involving diverse nuclear-nuclear incompatibilities, a mito-nuclear incompatibility, and maternal effects. These findings demonstrate that male sensitivity to genetic perturbation may be genetically simple compared to hybrid inviability in Caenorhabditis and motivates tests of generality for the genetic architecture of hybrid incompatibility across the breadth of phylogeny.

Parameter optimization of differential evolution algorithm for automatic playlist generation problem

NASA Astrophysics Data System (ADS)

Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

2017-11-01

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

Intrusion detection using rough set classification.

PubMed

Zhang, Lian-hua; Zhang, Guan-hua; Zhang, Jie; Bai, Ying-cai

2004-09-01

Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of "IF-THEN" rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

Guided particle swarm optimization method to solve general nonlinear optimization problems

NASA Astrophysics Data System (ADS)

Abdelhalim, Alyaa; Nakata, Kazuhide; El-Alem, Mahmoud; Eltawil, Amr

2018-04-01

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder-Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.

Intelligent design optimization of a shape-memory-alloy-actuated reconfigurable wing

NASA Astrophysics Data System (ADS)

Lagoudas, Dimitris C.; Strelec, Justin K.; Yen, John; Khan, Mohammad A.

2000-06-01

The unique thermal and mechanical properties offered by shape memory alloys (SMAs) present exciting possibilities in the field of aerospace engineering. When properly trained, SMA wires act as linear actuators by contracting when heated and returning to their original shape when cooled. It has been shown experimentally that the overall shape of an airfoil can be altered by activating several attached SMA wire actuators. This shape-change can effectively increase the efficiency of a wing in flight at several different flow regimes. To determine the necessary placement of these wire actuators within the wing, an optimization method that incorporates a fully-coupled structural, thermal, and aerodynamic analysis has been utilized. Due to the complexity of the fully-coupled analysis, intelligent optimization methods such as genetic algorithms have been used to efficiently converge to an optimal solution. The genetic algorithm used in this case is a hybrid version with global search and optimization capabilities augmented by the simplex method as a local search technique. For the reconfigurable wing, each chromosome represents a realizable airfoil configuration and its genes are the SMA actuators, described by their location and maximum transformation strain. The genetic algorithm has been used to optimize this design problem to maximize the lift-to-drag ratio for a reconfigured airfoil shape.

Order Batching in Warehouses by Minimizing Total Tardiness: A Hybrid Approach of Weighted Association Rule Mining and Genetic Algorithms

PubMed Central

Taheri, Shahrooz; Mat Saman, Muhamad Zameri; Wong, Kuan Yew

2013-01-01

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach. PMID:23864823

Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms.

PubMed

Azadnia, Amir Hossein; Taheri, Shahrooz; Ghadimi, Pezhman; Saman, Muhamad Zameri Mat; Wong, Kuan Yew

2013-01-01

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms

NASA Astrophysics Data System (ADS)

Gladwin, D.; Stewart, P.; Stewart, J.

2011-02-01

This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control structures.

Multiobjective synchronization of coupled systems

NASA Astrophysics Data System (ADS)

Tang, Yang; Wang, Zidong; Wong, W. K.; Kurths, Jürgen; Fang, Jian-an

2011-06-01

In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks.

New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

NASA Astrophysics Data System (ADS)

Bonakdari, Hossein; Zaji, Amir Hossein

2018-03-01

In many hydraulic structures, side weirs have a critical role. Accurately predicting the discharge coefficient is one of the most important stages in the side weir design process. In the present paper, a new high efficient side weir is investigated. To simulate the discharge coefficient of these side weirs, three novel soft computing methods are used. The process includes modeling the discharge coefficient with the hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) and three optimization algorithms, namely Differential Evaluation (ANFIS-DE), Genetic Algorithm (ANFIS-GA) and Particle Swarm Optimization (ANFIS-PSO). In addition, sensitivity analysis is done to find the most efficient input variables for modeling the discharge coefficient of these types of side weirs. According to the results, the ANFIS method has higher performance when using simpler input variables. In addition, the ANFIS-DE with RMSE of 0.077 has higher performance than the ANFIS-GA and ANFIS-PSO methods with RMSE of 0.079 and 0.096, respectively.

Numerical solution to generalized Burgers'-Fisher equation using Exp-function method hybridized with heuristic computation.

PubMed

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems.

Numerical Solution to Generalized Burgers'-Fisher Equation Using Exp-Function Method Hybridized with Heuristic Computation

PubMed Central

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems. PMID:25811858

A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

PubMed Central

Meskine, Fatiha; Chikr El Mezouar, Miloud; Taleb, Nasreddine

2010-01-01

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise. PMID:22163672

A rigid image registration based on the nonsubsampled contourlet transform and genetic algorithms.

PubMed

Meskine, Fatiha; Chikr El Mezouar, Miloud; Taleb, Nasreddine

2010-01-01

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise.

Evaluation of hybrid algorithm for analysis of scattered light using ex vivo nuclear morphology measurements of cervical epithelium

PubMed Central

Ho, Derek; Drake, Tyler K.; Bentley, Rex C.; Valea, Fidel A.; Wax, Adam

2015-01-01

We evaluate a new hybrid algorithm for determining nuclear morphology using angle-resolved low coherence interferometry (a/LCI) measurements in ex vivo cervical tissue. The algorithm combines Mie theory based and continuous wavelet transform inverse light scattering analysis. The hybrid algorithm was validated and compared to traditional Mie theory based analysis using an ex vivo tissue data set. The hybrid algorithm achieved 100% agreement with pathology in distinguishing dysplastic and non-dysplastic biopsy sites in the pilot study. Significantly, the new algorithm performed over four times faster than traditional Mie theory based analysis. PMID:26309741

Robust Global Image Registration Based on a Hybrid Algorithm Combining Fourier and Spatial Domain Techniques

DTIC Science & Technology

2012-09-01

Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain techniques Peter N. Crabtree, Collin Seanor...00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain...demonstrate performance of a hybrid algorithm . These results are from analysis of a set of images of an ISO 12233 [12] resolution chart captured in the

Hybrid flower pollination algorithm strategies for t-way test suite generation.

PubMed

Nasser, Abdullah B; Zamli, Kamal Z; Alsewari, AbdulRahman A; Ahmed, Bestoun S

2018-01-01

The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size.

Hybrid flower pollination algorithm strategies for t-way test suite generation

PubMed Central

Zamli, Kamal Z.; Alsewari, AbdulRahman A.

2018-01-01

The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size. PMID:29718918

A hybrid neurogenetic approach for stock forecasting.

PubMed

Kwon, Yung-Keun; Moon, Byung-Ro

2007-05-01

In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network (NN) having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm (GA) optimizes the NN's weights under a 2-D encoding and crossover. We devised a context-based ensemble method of NNs which dynamically changes on the basis of the test day's context. To reduce the time in processing mass data, we parallelized the GA on a Linux cluster system using message passing interface. We tested the proposed method with 36 companies in NYSE and NASDAQ for 13 years from 1992 to 2004. The neurogenetic hybrid showed notable improvement on the average over the buy-and-hold strategy and the context-based ensemble further improved the results. We also observed that some companies were more predictable than others, which implies that the proposed neurogenetic hybrid can be used for financial portfolio construction.

Derivation of Optimal Operating Rules for Large-scale Reservoir Systems Considering Multiple Trade-off

NASA Astrophysics Data System (ADS)

Zhang, J.; Lei, X.; Liu, P.; Wang, H.; Li, Z.

2017-12-01

Flood control operation of multi-reservoir systems such as parallel reservoirs and hybrid reservoirs often suffer from complex interactions and trade-off among tributaries and the mainstream. The optimization of such systems is computationally intensive due to nonlinear storage curves, numerous constraints and complex hydraulic connections. This paper aims to derive the optimal flood control operating rules based on the trade-off among tributaries and the mainstream using a new algorithm known as weighted non-dominated sorting genetic algorithm II (WNSGA II). WNSGA II could locate the Pareto frontier in non-dominated region efficiently due to the directed searching by weighted crowding distance, and the results are compared with those of conventional operating rules (COR) and single objective genetic algorithm (GA). Xijiang river basin in China is selected as a case study, with eight reservoirs and five flood control sections within four tributaries and the mainstream. Furthermore, the effects of inflow uncertainty have been assessed. Results indicate that: (1) WNSGA II could locate the non-dominated solutions faster and provide better Pareto frontier than the traditional non-dominated sorting genetic algorithm II (NSGA II) due to the weighted crowding distance; (2) WNSGA II outperforms COR and GA on flood control in the whole basin; (3) The multi-objective operating rules from WNSGA II deal with the inflow uncertainties better than COR. Therefore, the WNSGA II can be used to derive stable operating rules for large-scale reservoir systems effectively and efficiently.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

PubMed Central

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy

NASA Astrophysics Data System (ADS)

Wang, Yan; Huang, Song; Ji, Zhicheng

2017-07-01

This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.

Clavulanic acid production estimation based on color and structural features of Streptomyces clavuligerus bacteria using self-organizing map and genetic algorithm.

PubMed

Nurmohamadi, Maryam; Pourghassem, Hossein

2014-05-01

The utilization of antibiotics produced by Clavulanic acid (CA) is an increasing need in medicine and industry. Usually, the CA is created from the fermentation of Streptomycen Clavuligerus (SC) bacteria. Analysis of visual and morphological features of SC bacteria is an appropriate measure to estimate the growth of CA. In this paper, an automatic and fast CA production level estimation algorithm based on visual and structural features of SC bacteria instead of statistical methods and experimental evaluation by microbiologist is proposed. In this algorithm, structural features such as the number of newborn branches, thickness of hyphal and bacterial density and also color features such as acceptance color levels are extracted from the SC bacteria. Moreover, PH and biomass of the medium provided by microbiologists are considered as specified features. The level of CA production is estimated by using a new application of Self-Organizing Map (SOM), and a hybrid model of genetic algorithm with back propagation network (GA-BPN). The proposed algorithm is evaluated on four carbonic resources including malt, starch, wheat flour and glycerol that had used as different mediums of bacterial growth. Then, the obtained results are compared and evaluated with observation of specialist. Finally, the Relative Error (RE) for the SOM and GA-BPN are achieved 14.97% and 16.63%, respectively. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Solving SAT Problem Based on Hybrid Differential Evolution Algorithm

NASA Astrophysics Data System (ADS)

Liu, Kunqi; Zhang, Jingmin; Liu, Gang; Kang, Lishan

Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybrid differential evolution algorithm is proposed to solve the Satisfiability problem. It makes full use of strong local search capacity of hill-climbing algorithm and strong global search capability of differential evolution algorithm, which makes up their disadvantages, improves the efficiency of algorithm and avoids the stagnation phenomenon. The experiment results show that the hybrid algorithm is efficient in solving SAT problem.

On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

NASA Astrophysics Data System (ADS)

Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

2003-12-01

Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

Determining the optimal number of Kanban in multi-products supply chain system

NASA Astrophysics Data System (ADS)

Widyadana, G. A.; Wee, H. M.; Chang, Jer-Yuan

2010-02-01

Kanban, a key element of just-in-time system, is a re-order card or signboard giving instruction or triggering the pull system to manufacture or supply a component based on actual usage of material. There are two types of Kanban: production Kanban and withdrawal Kanban. This study uses optimal and meta-heuristic methods to determine the Kanban quantity and withdrawal lot sizes in a supply chain system. Although the mix integer programming method gives an optimal solution, it is not time efficient. For this reason, the meta-heuristic methods are suggested. In this study, a genetic algorithm (GA) and a hybrid of genetic algorithm and simulated annealing (GASA) are used. The study compares the performance of GA and GASA with that of the optimal method using MIP. The given problems show that both GA and GASA result in a near optimal solution, and they outdo the optimal method in term of run time. In addition, the GASA heuristic method gives a better performance than the GA heuristic method.

Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Yufei; Huang, Haibin

2014-02-01

A hybrid algorithm combining particle swarm optimization (PSO) algorithm with the Legendre pseudospectral method (LPM) is proposed for solving time-optimal trajectory planning problem of underactuated spacecrafts. At the beginning phase of the searching process, an initialization generator is constructed by the PSO algorithm due to its strong global searching ability and robustness to random initial values, however, PSO algorithm has a disadvantage that its convergence rate around the global optimum is slow. Then, when the change in fitness function is smaller than a predefined value, the searching algorithm is switched to the LPM to accelerate the searching process. Thus, with the obtained solutions by the PSO algorithm as a set of proper initial guesses, the hybrid algorithm can find a global optimum more quickly and accurately. 200 Monte Carlo simulations results demonstrate that the proposed hybrid PSO-LPM algorithm has greater advantages in terms of global searching capability and convergence rate than both single PSO algorithm and LPM algorithm. Moreover, the PSO-LPM algorithm is also robust to random initial values.

Development and Translation of Hybrid Optoacoustic/Ultrasonic Tomography for Early Breast Cancer Detection

DTIC Science & Technology

2014-09-01

to develop an optimized system design and associated image reconstruction algorithms for a hybrid three-dimensional (3D) breast imaging system that...research is to develop an optimized system design and associated image reconstruction algorithms for a hybrid three-dimensional (3D) breast imaging ...i) developed time-of- flight extraction algorithms to perform USCT, (ii) developing image reconstruction algorithms for USCT, (iii) developed

Local adaptation within a hybrid species

PubMed Central

Eroukhmanoff, F; Hermansen, J S; Bailey, R I; Sæther, S A; Sætre, G-P

2013-01-01

Ecological divergence among populations may be strongly influenced by their genetic background. For instance, genetic admixture through introgressive hybridization or hybrid speciation is likely to affect the genetic variation and evolvability of phenotypic traits. We studied geographic variation in two beak dimensions and three other phenotypic traits of the Italian sparrow (Passer italiae), a young hybrid species formed through interbreeding between house sparrows (P. domesticus) and Spanish sparrows (P. hispaniolensis). We found that beak morphology was strongly influenced by precipitation regimes and that it appeared to be the target of divergent selection within Italian sparrows. Interestingly, however, the degree of parental genetic contribution in the hybrid species had no effect on phenotypic beak variation. Moreover, beak height divergence may mediate genetic differentiation between populations, consistent with isolation-by-adaptation within this hybrid species. The study illustrates how hybrid species may be relatively unconstrained by their admixed genetic background, allowing them to adapt rapidly to environmental variation. PMID:23695379

Optimization of the ANFIS using a genetic algorithm for physical work rate classification.

PubMed

Habibi, Ehsanollah; Salehi, Mina; Yadegarfar, Ghasem; Taheri, Ali

2018-03-13

Recently, a new method was proposed for physical work rate classification based on an adaptive neuro-fuzzy inference system (ANFIS). This study aims to present a genetic algorithm (GA)-optimized ANFIS model for a highly accurate classification of physical work rate. Thirty healthy men participated in this study. Directly measured heart rate and oxygen consumption of the participants in the laboratory were used for training the ANFIS classifier model in MATLAB version 8.0.0 using a hybrid algorithm. A similar process was done using the GA as an optimization technique. The accuracy, sensitivity and specificity of the ANFIS classifier model were increased successfully. The mean accuracy of the model was increased from 92.95 to 97.92%. Also, the calculated root mean square error of the model was reduced from 5.4186 to 3.1882. The maximum estimation error of the optimized ANFIS during the network testing process was ± 5%. The GA can be effectively used for ANFIS optimization and leads to an accurate classification of physical work rate. In addition to high accuracy, simple implementation and inter-individual variability consideration are two other advantages of the presented model.

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

PubMed Central

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs). PMID:29662297

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

PubMed

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

A Theoretical Analysis of Why Hybrid Ensembles Work.

PubMed

Hsu, Kuo-Wei

2017-01-01

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system

NASA Astrophysics Data System (ADS)

Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.

2012-05-01

The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.

Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

NASA Astrophysics Data System (ADS)

Arotaritei, D.; Rotariu, C.

2015-09-01

In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).

A hybrid artificial bee colony algorithm and pattern search method for inversion of particle size distribution from spectral extinction data

NASA Astrophysics Data System (ADS)

Wang, Li; Li, Feng; Xing, Jian

2017-10-01

In this paper, a hybrid artificial bee colony (ABC) algorithm and pattern search (PS) method is proposed and applied for recovery of particle size distribution (PSD) from spectral extinction data. To be more useful and practical, size distribution function is modelled as the general Johnson's ? function that can overcome the difficulty of not knowing the exact type beforehand encountered in many real circumstances. The proposed hybrid algorithm is evaluated through simulated examples involving unimodal, bimodal and trimodal PSDs with different widths and mean particle diameters. For comparison, all examples are additionally validated by the single ABC algorithm. In addition, the performance of the proposed algorithm is further tested by actual extinction measurements with real standard polystyrene samples immersed in water. Simulation and experimental results illustrate that the hybrid algorithm can be used as an effective technique to retrieve the PSDs with high reliability and accuracy. Compared with the single ABC algorithm, our proposed algorithm can produce more accurate and robust inversion results while taking almost comparative CPU time over ABC algorithm alone. The superiority of ABC and PS hybridization strategy in terms of reaching a better balance of estimation accuracy and computation effort increases its potentials as an excellent inversion technique for reliable and efficient actual measurement of PSD.

Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

PubMed Central

Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Garshasbi, Masoud

2018-01-01

Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier. Fuzzy logic is used to infer the importance of each sample in the training phase and decrease the outlier sensitivity of the system to increase the ability to generalize the classifier. A decision-tree algorithm was applied to the most frequent genes to develop a set of rules for each type of cancer. This improved the abilities of the algorithm by finding the best parameters for the classifier during the training phase without the need for trial-and-error by the user. The proposed approach was tested on four benchmark gene expression profiles. Results: Good results have been demonstrated for the proposed algorithm. The classification accuracy for leukemia data is 100%, for colon cancer is 96.67% and for breast cancer is 98%. The results show that the best kernel used in training the SVM classifier is the radial basis function. Conclusions: The experimental results show that the proposed algorithm can decrease the dimensionality of the dataset, determine the most informative gene subset, and improve classification accuracy using the optimal parameters of the classifier with no user interface. PMID:29535919

Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

NASA Astrophysics Data System (ADS)

Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan

2015-03-01

With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.

A Theoretical Analysis of Why Hybrid Ensembles Work

PubMed Central

2017-01-01

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles. PMID:28255296

The Rational Hybrid Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Clark, Michael

2006-12-01

The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.

Optimizing the static-dynamic performance of the body-in-white using a modified non-dominated sorting genetic algorithm coupled with grey relational analysis

NASA Astrophysics Data System (ADS)

Wang, Dengfeng; Cai, Kefang

2018-04-01

This article presents a hybrid method combining a modified non-dominated sorting genetic algorithm (MNSGA-II) with grey relational analysis (GRA) to improve the static-dynamic performance of a body-in-white (BIW). First, an implicit parametric model of the BIW was built using SFE-CONCEPT software, and then the validity of the implicit parametric model was verified by physical testing. Eight shape design variables were defined for BIW beam structures based on the implicit parametric technology. Subsequently, MNSGA-II was used to determine the optimal combination of the design parameters that can improve the bending stiffness, torsion stiffness and low-order natural frequencies of the BIW without considerable increase in the mass. A set of non-dominated solutions was then obtained in the multi-objective optimization design. Finally, the grey entropy theory and GRA were applied to rank all non-dominated solutions from best to worst to determine the best trade-off solution. The comparison between the GRA and the technique for order of preference by similarity to ideal solution (TOPSIS) illustrated the reliability and rationality of GRA. Moreover, the effectiveness of the hybrid method was verified by the optimal results such that the bending stiffness, torsion stiffness, first order bending and first order torsion natural frequency were improved by 5.46%, 9.30%, 7.32% and 5.73%, respectively, with the mass of the BIW increasing by 1.30%.

Regulatory divergence of X-linked genes and hybrid male sterility in mice.

PubMed

Oka, Ayako; Shiroishi, Toshihiko

2014-01-01

Postzygotic reproductive isolation is the reduction of fertility or viability in hybrids between genetically diverged populations. One example of reproductive isolation, hybrid male sterility, may be caused by genetic incompatibility between diverged genetic factors in two distinct populations. Genetic factors involved in hybrid male sterility are disproportionately located on the X chromosome. Recent studies showing the evolutionary divergence in gene regulatory networks or epigenetic effects suggest that the genetic incompatibilities occur at much broader levels than had previously been thought (e.g., incompatibility of protein-protein interactions). The latest studies suggest that evolutionary divergence of transcriptional regulation causes genetic incompatibilities in hybrid animals, and that such incompatibilities preferentially involve X-linked genes. In this review, we focus on recent progress in understanding hybrid sterility in mice, including our studies, and we discuss the evolutionary significance of regulatory divergence for speciation.

Evolutionary computing based approach for the removal of ECG artifact from the corrupted EEG signal.

PubMed

Priyadharsini, S Suja; Rajan, S Edward

2014-01-01

Electroencephalogram (EEG) is an important tool for clinical diagnosis of brain-related disorders and problems. However, it is corrupted by various biological artifacts, of which ECG is one among them that reduces the clinical importance of EEG especially for epileptic patients and patients with short neck. To remove the ECG artifact from the measured EEG signal using an evolutionary computing approach based on the concept of Hybrid Adaptive Neuro-Fuzzy Inference System, which helps the Neurologists in the diagnosis and follow-up of encephalopathy. The proposed hybrid learning methods are ANFIS-MA and ANFIS-GA, which uses Memetic Algorithm (MA) and Genetic algorithm (GA) for tuning the antecedent and consequent part of the ANFIS structure individually. The performances of the proposed methods are compared with that of ANFIS and adaptive Recursive Least Squares (RLS) filtering algorithm. The proposed methods are experimentally validated by applying it to the simulated data sets, subjected to non-linearity condition and real polysomonograph data sets. Performance metrics such as sensitivity, specificity and accuracy of the proposed method ANFIS-MA, in terms of correction rate are found to be 93.8%, 100% and 99% respectively, which is better than current state-of-the-art approaches. The evaluation process used and demonstrated effectiveness of the proposed method proves that ANFIS-MA is more effective in suppressing ECG artifacts from the corrupted EEG signals than ANFIS-GA, ANFIS and RLS algorithm.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.

PubMed

Valavanis, Ioannis K; Mougiakakou, Stavroula G; Grimaldi, Keith A; Nikita, Konstantina S

2010-09-08

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.

Assessing genetic divergence in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism markers.

PubMed

Zhang, F; Ge, Y Y; Wang, W Y; Shen, X L; Yu, X Y

2012-12-03

Conventional hybridization and selection techniques have aided the development of new ornamental crop cultivars. However, little information is available on the genetic divergence of bromeliad hybrids. In the present study, we investigated the genetic variability in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism (SRAP) markers. The morphological analysis showed that the putative hybrids were intermediate between both parental species with respect to inflorescence characteristics. The 16 SRAP primer combinations yield 265 bands, among which 154 (57.72%) were polymorphic. The genetic similarity was an average of 0.59 and ranged from 0.21 to 0.87, indicating moderate genetic divergence among the hybrids. The unweighted pair group method with arithmetic average (UPGMA)-based cluster analysis distinguished the hybrids from their parents with a genetic distance coefficient of 0.54. The cophenetic correlation was 0.93, indicating a good fit between the dendrogram and the original distance matrix. The two-dimensional plot from the principal coordinate analysis showed that the hybrids were intermediately dispersed between both parents, corresponding to the results of the UPGMA cluster and the morphological analysis. These results suggest that SRAP markers could help to identify breeders, characterize F(1) hybrids of bromeliads at an early stage, and expedite genetic improvement of bromeliad cultivars.

AMLSA Algorithm for Hybrid Precoding in Millimeter Wave MIMO Systems

NASA Astrophysics Data System (ADS)

Liu, Fulai; Sun, Zhenxing; Du, Ruiyan; Bai, Xiaoyu

2017-10-01

In this paper, an effective algorithm will be proposed for hybrid precoding in mmWave MIMO systems, referred to as alternating minimization algorithm with the least squares amendment (AMLSA algorithm). To be specific, for the fully-connected structure, the presented algorithm is exploited to minimize the classical objective function and obtain the hybrid precoding matrix. It introduces an orthogonal constraint to the digital precoding matrix which is amended subsequently by the least squares after obtaining its alternating minimization iterative result. Simulation results confirm that the achievable spectral efficiency of our proposed algorithm is better to some extent than that of the existing algorithm without the least squares amendment. Furthermore, the number of iterations is reduced slightly via improving the initialization procedure.

A hybrid algorithm for speckle noise reduction of ultrasound images.

PubMed

Singh, Karamjeet; Ranade, Sukhjeet Kaur; Singh, Chandan

2017-09-01

Medical images are contaminated by multiplicative speckle noise which significantly reduce the contrast of ultrasound images and creates a negative effect on various image interpretation tasks. In this paper, we proposed a hybrid denoising approach which collaborate the both local and nonlocal information in an efficient manner. The proposed hybrid algorithm consist of three stages in which at first stage the use of local statistics in the form of guided filter is used to reduce the effect of speckle noise initially. Then, an improved speckle reducing bilateral filter (SRBF) is developed to further reduce the speckle noise from the medical images. Finally, to reconstruct the diffused edges we have used the efficient post-processing technique which jointly considered the advantages of both bilateral and nonlocal mean (NLM) filter for the attenuation of speckle noise efficiently. The performance of proposed hybrid algorithm is evaluated on synthetic, simulated and real ultrasound images. The experiments conducted on various test images demonstrate that our proposed hybrid approach outperforms the various traditional speckle reduction approaches included recently proposed NLM and optimized Bayesian-based NLM. The results of various quantitative, qualitative measures and by visual inspection of denoise synthetic and real ultrasound images demonstrate that the proposed hybrid algorithm have strong denoising capability and able to preserve the fine image details such as edge of a lesion better than previously developed methods for speckle noise reduction. The denoising and edge preserving capability of hybrid algorithm is far better than existing traditional and recently proposed speckle reduction (SR) filters. The success of proposed algorithm would help in building the lay foundation for inventing the hybrid algorithms for denoising of ultrasound images. Copyright © 2017 Elsevier B.V. All rights reserved.

Enhanced Energy Localization in Hyperthermia Treatment Based on Hybrid Electromagnetic and Ultrasonic System: Proof of Concept with Numerical Simulations.

PubMed

Nizam-Uddin, N; Elshafiey, Ibrahim

2017-01-01

This paper proposes a hybrid hyperthermia treatment system, utilizing two noninvasive modalities for treating brain tumors. The proposed system depends on focusing electromagnetic (EM) and ultrasound (US) energies. The EM hyperthermia subsystem enhances energy localization by incorporating a multichannel wideband setting and coherent-phased-array technique. A genetic algorithm based optimization tool is developed to enhance the specific absorption rate (SAR) distribution by reducing hotspots and maximizing energy deposition at tumor regions. The treatment performance is also enhanced by augmenting an ultrasonic subsystem to allow focused energy deposition into deep tumors. The therapeutic faculty of ultrasonic energy is assessed by examining the control of mechanical alignment of transducer array elements. A time reversal (TR) approach is then investigated to address challenges in energy focus in both subsystems. Simulation results of the synergetic effect of both modalities assuming a simplified model of human head phantom demonstrate the feasibility of the proposed hybrid technique as a noninvasive tool for thermal treatment of brain tumors.

Enhanced Energy Localization in Hyperthermia Treatment Based on Hybrid Electromagnetic and Ultrasonic System: Proof of Concept with Numerical Simulations

PubMed Central

Elshafiey, Ibrahim

2017-01-01

This paper proposes a hybrid hyperthermia treatment system, utilizing two noninvasive modalities for treating brain tumors. The proposed system depends on focusing electromagnetic (EM) and ultrasound (US) energies. The EM hyperthermia subsystem enhances energy localization by incorporating a multichannel wideband setting and coherent-phased-array technique. A genetic algorithm based optimization tool is developed to enhance the specific absorption rate (SAR) distribution by reducing hotspots and maximizing energy deposition at tumor regions. The treatment performance is also enhanced by augmenting an ultrasonic subsystem to allow focused energy deposition into deep tumors. The therapeutic faculty of ultrasonic energy is assessed by examining the control of mechanical alignment of transducer array elements. A time reversal (TR) approach is then investigated to address challenges in energy focus in both subsystems. Simulation results of the synergetic effect of both modalities assuming a simplified model of human head phantom demonstrate the feasibility of the proposed hybrid technique as a noninvasive tool for thermal treatment of brain tumors. PMID:28840125

HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

NASA Astrophysics Data System (ADS)

Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

2016-07-01

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

File text security using Hybrid Cryptosystem with Playfair Cipher Algorithm and Knapsack Naccache-Stern Algorithm

NASA Astrophysics Data System (ADS)

Amalia; Budiman, M. A.; Sitepu, R.

2018-03-01

Cryptography is one of the best methods to keep the information safe from security attack by unauthorized people. At present, Many studies had been done by previous researchers to generate a more robust cryptographic algorithm to provide high security for data communication. To strengthen data security, one of the methods is hybrid cryptosystem method that combined symmetric and asymmetric algorithm. In this study, we observed a hybrid cryptosystem method contain Modification Playfair Cipher 16x16 algorithm as a symmetric algorithm and Knapsack Naccache-Stern as an asymmetric algorithm. We observe a running time of this hybrid algorithm with some of the various experiments. We tried different amount of characters to be tested which are 10, 100, 1000, 10000 and 100000 characters and we also examined the algorithm with various key’s length which are 10, 20, 30, 40 of key length. The result of our study shows that the processing time for encryption and decryption process each algorithm is linearly proportional, it means the longer messages character then, the more significant times needed to encrypt and decrypt the messages. The encryption running time of Knapsack Naccache-Stern algorithm takes a longer time than its decryption, while the encryption running time of modification Playfair Cipher 16x16 algorithm takes less time than its decryption.

First-Order Interfacial Transformations with a Critical Point: Breaking the Symmetry at a Symmetric Tilt Grain Boundary

NASA Astrophysics Data System (ADS)

Yang, Shengfeng; Zhou, Naixie; Zheng, Hui; Ong, Shyue Ping; Luo, Jian

2018-02-01

First-order interfacial phaselike transformations that break the mirror symmetry of the symmetric ∑5 (210 ) tilt grain boundary (GB) are discovered by combining a modified genetic algorithm with hybrid Monte Carlo and molecular dynamics simulations. Density functional theory calculations confirm this prediction. This first-order coupled structural and adsorption transformation, which produces two variants of asymmetric bilayers, vanishes at an interfacial critical point. A GB complexion (phase) diagram is constructed via semigrand canonical ensemble atomistic simulations for the first time.

An enhanced DWBA algorithm in hybrid WDM/TDM EPON networks with heterogeneous propagation delays

NASA Astrophysics Data System (ADS)

Li, Chengjun; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng

2011-12-01

An enhanced dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM PON is proposed and experimentally demonstrated. In addition to the fairness of bandwidth allocation, this algorithm also considers the varying propagation delays between ONUs and OLT. The simulation based on MATLAB indicates that the improved algorithm has a better performance compared with some other algorithms.

Assessing copy number from exome sequencing and exome array CGH based on CNV spectrum in a large clinical cohort.

PubMed

Retterer, Kyle; Scuffins, Julie; Schmidt, Daniel; Lewis, Rachel; Pineda-Alvarez, Daniel; Stafford, Amanda; Schmidt, Lindsay; Warren, Stephanie; Gibellini, Federica; Kondakova, Anastasia; Blair, Amanda; Bale, Sherri; Matyakhina, Ludmila; Meck, Jeanne; Aradhya, Swaroop; Haverfield, Eden

2015-08-01

Detection of copy-number variation (CNV) is important for investigating many genetic disorders. Testing a large clinical cohort by array comparative genomic hybridization provides a deep perspective on the spectrum of pathogenic CNV. In this context, we describe a bioinformatics approach to extract CNV information from whole-exome sequencing and demonstrate its utility in clinical testing. Exon-focused arrays and whole-genome chromosomal microarray analysis were used to test 14,228 and 14,000 individuals, respectively. Based on these results, we developed an algorithm to detect deletions/duplications in whole-exome sequencing data and a novel whole-exome array. In the exon array cohort, we observed a positive detection rate of 2.4% (25 duplications, 318 deletions), of which 39% involved one or two exons. Chromosomal microarray analysis identified 3,345 CNVs affecting single genes (18%). We demonstrate that our whole-exome sequencing algorithm resolves CNVs of three or more exons. These results demonstrate the clinical utility of single-exon resolution in CNV assays. Our whole-exome sequencing algorithm approaches this resolution but is complemented by a whole-exome array to unambiguously identify intragenic CNVs and single-exon changes. These data illustrate the next advancements in CNV analysis through whole-exome sequencing and whole-exome array.Genet Med 17 8, 623-629.

Genome-wide mapping in a house mouse hybrid zone reveals hybrid sterility loci and Dobzhansky-Muller interactions.

PubMed

Turner, Leslie M; Harr, Bettina

2014-12-09

Mapping hybrid defects in contact zones between incipient species can identify genomic regions contributing to reproductive isolation and reveal genetic mechanisms of speciation. The house mouse features a rare combination of sophisticated genetic tools and natural hybrid zones between subspecies. Male hybrids often show reduced fertility, a common reproductive barrier between incipient species. Laboratory crosses have identified sterility loci, but each encompasses hundreds of genes. We map genetic determinants of testis weight and testis gene expression using offspring of mice captured in a hybrid zone between M. musculus musculus and M. m. domesticus. Many generations of admixture enables high-resolution mapping of loci contributing to these sterility-related phenotypes. We identify complex interactions among sterility loci, suggesting multiple, non-independent genetic incompatibilities contribute to barriers to gene flow in the hybrid zone.

A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu

Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.

Degree of Hybridization in Seed Stands of Pinus engelmannii Carr. In the Sierra Madre Occidental, Durango, Mexico

PubMed Central

Ávila-Flores, Israel Jaime; Hernández-Díaz, José Ciro; González-Elizondo, Maria Socorro; Prieto-Ruíz, José Ángel; Wehenkel, Christian

2016-01-01

Hybridization is an important evolutionary force, because interspecific gene transfer can introduce more new genetic material than is directly generated by mutations. Pinus engelmannii Carr. is one of the nine most common pine species in the pine-oak forest ecoregion in the state of Durango, Mexico. This species is widely harvested for lumber and is also used in reforestation programmes. Interspecific hybrids between P.engelmannii and Pinus arizonica Engelm. have been detected by morphological analysis. The presence of hybrids in P. engelmannii seed stands may affect seed quality and reforestation success. Therefore, the goals of this research were to identify introgressive hybridization between P. engelmannii and other pine species in eight seed stands of this species in Durango, Mexico, and to examine how hybrid proportion is related to mean genetic dissimilarity between trees in these stands, using Amplified Fragment Length Polymorphism (AFLP) markers and morphological traits. Differences in the average current annual increment of putative hybrids and pure trees were also tested for statistical significance. Morphological and genetic analyses of 280 adult trees were carried out. Putative hybrids were found in all the seed stands studied. The hybrids did not differ from the pure trees in vigour or robustness. All stands with putative P. engelmannii hybrids detected by both AFLPs and morphological traits showed the highest average values of the Tanimoto distance, which indicates: i) more heterogeneous genetic material, ii) higher genetic variation and therefore iii) the higher evolutionary potential of these stands, and iv) that the morphological differentiation (hybrid/not hybrid) is strongly associated with the Tanimoto distance per stand. We conclude that natural pairwise hybrids are very common in the studied stands. Both morphological and molecular approaches are necessary to confirm the genetic identity of forest reproductive material. PMID:27064490

Degree of Hybridization in Seed Stands of Pinus engelmannii Carr. In the Sierra Madre Occidental, Durango, Mexico.

PubMed

Ávila-Flores, Israel Jaime; Hernández-Díaz, José Ciro; González-Elizondo, Maria Socorro; Prieto-Ruíz, José Ángel; Wehenkel, Christian

2016-01-01

Hybridization is an important evolutionary force, because interspecific gene transfer can introduce more new genetic material than is directly generated by mutations. Pinus engelmannii Carr. is one of the nine most common pine species in the pine-oak forest ecoregion in the state of Durango, Mexico. This species is widely harvested for lumber and is also used in reforestation programmes. Interspecific hybrids between P.engelmannii and Pinus arizonica Engelm. have been detected by morphological analysis. The presence of hybrids in P. engelmannii seed stands may affect seed quality and reforestation success. Therefore, the goals of this research were to identify introgressive hybridization between P. engelmannii and other pine species in eight seed stands of this species in Durango, Mexico, and to examine how hybrid proportion is related to mean genetic dissimilarity between trees in these stands, using Amplified Fragment Length Polymorphism (AFLP) markers and morphological traits. Differences in the average current annual increment of putative hybrids and pure trees were also tested for statistical significance. Morphological and genetic analyses of 280 adult trees were carried out. Putative hybrids were found in all the seed stands studied. The hybrids did not differ from the pure trees in vigour or robustness. All stands with putative P. engelmannii hybrids detected by both AFLPs and morphological traits showed the highest average values of the Tanimoto distance, which indicates: i) more heterogeneous genetic material, ii) higher genetic variation and therefore iii) the higher evolutionary potential of these stands, and iv) that the morphological differentiation (hybrid/not hybrid) is strongly associated with the Tanimoto distance per stand. We conclude that natural pairwise hybrids are very common in the studied stands. Both morphological and molecular approaches are necessary to confirm the genetic identity of forest reproductive material.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marchetti, Luca, E-mail: marchetti@cosbi.eu; Priami, Corrado, E-mail: priami@cosbi.eu; University of Trento, Department of Mathematics

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance andmore » accuracy of HRSSA against other state of the art algorithms.« less

An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems

NASA Astrophysics Data System (ADS)

Qian, Xiaohong; Wang, Xumei; Su, Yonghong; He, Liu

2018-04-01

There are many different algorithms for solving complex optimization problems. Each algorithm has been applied successfully in solving some optimization problems, but not efficiently in other problems. In this paper the Cauchy mutation and the multi-parent hybrid operator are combined to propose a hybrid evolutionary algorithm based on the communication (Mixed Evolutionary Algorithm based on Communication), hereinafter referred to as CMEA. The basic idea of the CMEA algorithm is that the initial population is divided into two subpopulations. Cauchy mutation operators and multiple paternal crossover operators are used to perform two subpopulations parallelly to evolve recursively until the downtime conditions are met. While subpopulation is reorganized, the individual is exchanged together with information. The algorithm flow is given and the performance of the algorithm is compared using a number of standard test functions. Simulation results have shown that this algorithm converges significantly faster than FEP (Fast Evolutionary Programming) algorithm, has good performance in global convergence and stability and is superior to other compared algorithms.

a New Hybrid Yin-Yang Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems

NASA Astrophysics Data System (ADS)

Heidari, A. A.; Kazemizade, O.; Hakimpour, F.

2017-09-01

Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only two initial positions and can produce more points with regard to the dimension of target problem. Due to unique advantages of these methodologies and to mitigate the immature convergence and local optima (LO) stagnation problems in PSO, in this work, a continuous hybrid strategy based on the behaviors of PSO and YYPO is proposed to attain the suboptimal solutions of uncapacitated warehouse location (UWL) problems. This efficient hierarchical PSO-based optimizer (PSOYPO) can improve the effectiveness of PSO on spatial optimization tasks such as the family of UWL problems. The performance of the proposed PSOYPO is verified according to some UWL benchmark cases. These test cases have been used in several works to evaluate the efficacy of different MA. Then, the PSOYPO is compared to the standard PSO, genetic algorithm (GA), harmony search (HS), modified HS (OBCHS), and evolutionary simulated annealing (ESA). The experimental results demonstrate that the PSOYPO can reveal a better or competitive efficacy compared to the PSO and other MA.

Genotype-Specific Minimal Residual Disease Interpretation Improves Stratification in Pediatric Acute Lymphoblastic Leukemia

PubMed Central

O’Connor, David; Enshaei, Amir; Bartram, Jack; Hancock, Jeremy; Harrison, Christine J.; Hough, Rachael; Samarasinghe, Sujith; Schwab, Claire; Vora, Ajay; Wade, Rachel; Moppett, John; Moorman, Anthony V.; Goulden, Nick

2018-01-01

Purpose Minimal residual disease (MRD) and genetic abnormalities are important risk factors for outcome in acute lymphoblastic leukemia. Current risk algorithms dichotomize MRD data and do not assimilate genetics when assigning MRD risk, which reduces predictive accuracy. The aim of our study was to exploit the full power of MRD by examining it as a continuous variable and to integrate it with genetics. Patients and Methods We used a population-based cohort of 3,113 patients who were treated in UKALL2003, with a median follow-up of 7 years. MRD was evaluated by polymerase chain reaction analysis of Ig/TCR gene rearrangements, and patients were assigned to a genetic subtype on the basis of immunophenotype, cytogenetics, and fluorescence in situ hybridization. To examine response kinetics at the end of induction, we log-transformed the absolute MRD value and examined its distribution across subgroups. Results MRD was log normally distributed at the end of induction. MRD distributions of patients with distinct genetic subtypes were different (P < .001). Patients with good-risk cytogenetics demonstrated the fastest disease clearance, whereas patients with high-risk genetics and T-cell acute lymphoblastic leukemia responded more slowly. The risk of relapse was correlated with MRD kinetics, and each log reduction in disease level reduced the risk by 20% (hazard ratio, 0.80; 95% CI, 0.77 to 0.83; P < .001). Although the risk of relapse was directly proportional to the MRD level within each genetic risk group, absolute relapse rate that was associated with a specific MRD value or category varied significantly by genetic subtype. Integration of genetic subtype–specific MRD values allowed more refined risk group stratification. Conclusion A single threshold for assigning patients to an MRD risk group does not reflect the response kinetics of the different genetic subtypes. Future risk algorithms should integrate genetics with MRD to accurately identify patients with the lowest and highest risk of relapse. PMID:29131699

A fast hybrid algorithm combining regularized motion tracking and predictive search for reducing the occurrence of large displacement errors.

PubMed

Jiang, Jingfeng; Hall, Timothy J

2011-04-01

A hybrid approach that inherits both the robustness of the regularized motion tracking approach and the efficiency of the predictive search approach is reported. The basic idea is to use regularized speckle tracking to obtain high-quality seeds in an explorative search that can be used in the subsequent intelligent predictive search. The performance of the hybrid speckle-tracking algorithm was compared with three published speckle-tracking methods using in vivo breast lesion data. We found that the hybrid algorithm provided higher displacement quality metric values, lower root mean squared errors compared with a locally smoothed displacement field, and higher improvement ratios compared with the classic block-matching algorithm. On the basis of these comparisons, we concluded that the hybrid method can further enhance the accuracy of speckle tracking compared with its real-time counterparts, at the expense of slightly higher computational demands. © 2011 IEEE

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

PubMed Central

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. PMID:25961028

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

PubMed

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

Genetic algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steven E.

1991-01-01

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

A hybrid optimization algorithm to explore atomic configurations of TiO 2 nanoparticles

DOE PAGES

Inclan, Eric J.; Geohegan, David B.; Yoon, Mina

2017-10-17

Here in this paper we present a hybrid algorithm comprised of differential evolution, coupled with the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton optimization algorithm, for the purpose of identifying a broad range of (meta)stable Ti nO 2n nanoparticles, as an example system, described by Buckingham interatomic potential. The potential and its gradient are modified to be piece-wise continuous to enable use of these continuous-domain, unconstrained algorithms, thereby improving compatibility. To measure computational effectiveness a regression on known structures is used. This approach defines effectiveness as the ability of an algorithm to produce a set of structures whose energy distribution follows the regression as themore » number of Ti nO 2n increases such that the shape of the distribution is consistent with the algorithm’s stated goals. Our calculation demonstrates that the hybrid algorithm finds global minimum configurations more effectively than the differential evolution algorithms, widely employed in the field of materials science. Specifically, the hybrid algorithm is shown to reproduce the global minimum energy structures reported in the literature up to n = 5, and retains good agreement with the regression up to n = 25. For 25 < n < 100, where literature structures are unavailable, the hybrid effectively obtains structures that are in lower energies per TiO 2 unit as the system size increases.« less

Hybridization and extinction.

PubMed

Todesco, Marco; Pascual, Mariana A; Owens, Gregory L; Ostevik, Katherine L; Moyers, Brook T; Hübner, Sariel; Heredia, Sylvia M; Hahn, Min A; Caseys, Celine; Bock, Dan G; Rieseberg, Loren H

2016-08-01

Hybridization may drive rare taxa to extinction through genetic swamping, where the rare form is replaced by hybrids, or by demographic swamping, where population growth rates are reduced due to the wasteful production of maladaptive hybrids. Conversely, hybridization may rescue the viability of small, inbred populations. Understanding the factors that contribute to destructive versus constructive outcomes of hybridization is key to managing conservation concerns. Here, we survey the literature for studies of hybridization and extinction to identify the ecological, evolutionary, and genetic factors that critically affect extinction risk through hybridization. We find that while extinction risk is highly situation dependent, genetic swamping is much more frequent than demographic swamping. In addition, human involvement is associated with increased risk and high reproductive isolation with reduced risk. Although climate change is predicted to increase the risk of hybridization-induced extinction, we find little empirical support for this prediction. Similarly, theoretical and experimental studies imply that genetic rescue through hybridization may be equally or more probable than demographic swamping, but our literature survey failed to support this claim. We conclude that halting the introduction of hybridization-prone exotics and restoring mature and diverse habitats that are resistant to hybrid establishment should be management priorities.

Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias

PubMed Central

2012-01-01

Background High-density genotyping arrays that measure hybridization of genomic DNA fragments to allele-specific oligonucleotide probes are widely used to genotype single nucleotide polymorphisms (SNPs) in genetic studies, including human genome-wide association studies. Hybridization intensities are converted to genotype calls by clustering algorithms that assign each sample to a genotype class at each SNP. Data for SNP probes that do not conform to the expected pattern of clustering are often discarded, contributing to ascertainment bias and resulting in lost information - as much as 50% in a recent genome-wide association study in dogs. Results We identified atypical patterns of hybridization intensities that were highly reproducible and demonstrated that these patterns represent genetic variants that were not accounted for in the design of the array platform. We characterized variable intensity oligonucleotide (VINO) probes that display such patterns and are found in all hybridization-based genotyping platforms, including those developed for human, dog, cattle, and mouse. When recognized and properly interpreted, VINOs recovered a substantial fraction of discarded probes and counteracted SNP ascertainment bias. We developed software (MouseDivGeno) that identifies VINOs and improves the accuracy of genotype calling. MouseDivGeno produced highly concordant genotype calls when compared with other methods but it uniquely identified more than 786000 VINOs in 351 mouse samples. We used whole-genome sequence from 14 mouse strains to confirm the presence of novel variants explaining 28000 VINOs in those strains. We also identified VINOs in human HapMap 3 samples, many of which were specific to an African population. Incorporating VINOs in phylogenetic analyses substantially improved the accuracy of a Mus species tree and local haplotype assignment in laboratory mouse strains. Conclusion The problems of ascertainment bias and missing information due to genotyping errors are widely recognized as limiting factors in genetic studies. We have conducted the first formal analysis of the effect of novel variants on genotyping arrays, and we have shown that these variants account for a large portion of miscalled and uncalled genotypes. Genetic studies will benefit from substantial improvements in the accuracy of their results by incorporating VINOs in their analyses. PMID:22260749

Design Optimization of a Hybrid Electric Vehicle Powertrain

NASA Astrophysics Data System (ADS)

Mangun, Firdause; Idres, Moumen; Abdullah, Kassim

2017-03-01

This paper presents an optimization work on hybrid electric vehicle (HEV) powertrain using Genetic Algorithm (GA) method. It focused on optimization of the parameters of powertrain components including supercapacitors to obtain maximum fuel economy. Vehicle modelling is based on Quasi-Static-Simulation (QSS) backward-facing approach. A combined city (FTP-75)-highway (HWFET) drive cycle is utilized for the design process. Seeking global optimum solution, GA was executed with different initial settings to obtain sets of optimal parameters. Starting from a benchmark HEV, optimization results in a smaller engine (2 l instead of 3 l) and a larger battery (15.66 kWh instead of 2.01 kWh). This leads to a reduction of 38.3% in fuel consumption and 30.5% in equivalent fuel consumption. Optimized parameters are also compared with actual values for HEV in the market.

Bio-inspired computational heuristics to study Lane-Emden systems arising in astrophysics model.

PubMed

Ahmad, Iftikhar; Raja, Muhammad Asif Zahoor; Bilal, Muhammad; Ashraf, Farooq

2016-01-01

This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane-Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.

Performance Analysis of Hybrid Electric Vehicle over Different Driving Cycles

NASA Astrophysics Data System (ADS)

Panday, Aishwarya; Bansal, Hari Om

2017-02-01

Article aims to find the nature and response of a hybrid vehicle on various standard driving cycles. Road profile parameters play an important role in determining the fuel efficiency. Typical parameters of road profile can be reduced to a useful smaller set using principal component analysis and independent component analysis. Resultant data set obtained after size reduction may result in more appropriate and important parameter cluster. With reduced parameter set fuel economies over various driving cycles, are ranked using TOPSIS and VIKOR multi-criteria decision making methods. The ranking trend is then compared with the fuel economies achieved after driving the vehicle over respective roads. Control strategy responsible for power split is optimized using genetic algorithm. 1RC battery model and modified SOC estimation method are considered for the simulation and improved results compared with the default are obtained.

Minimisation of the LCOE for the hybrid power supply system with the lead-acid battery

NASA Astrophysics Data System (ADS)

Kasprzyk, Leszek; Tomczewski, Andrzej; Bednarek, Karol; Bugała, Artur

2017-10-01

The paper presents the methodology of minimisation of the unit cost of production of energy generated in the hybrid system compatible with the lead-acid battery, and used to power a load with the known daily load curve. For this purpose, the objective function in the form of the LCOE and the genetic algorithm method were used. Simulation tests for three types of load with set daily load characteristics were performed. By taking advantage of the legal regulations applicable in the territory of Poland, regarding the energy storing in the power system, the optimal structure of the prosumer solar-wind system including the lead-acid battery, which meets the condition of maximum rated power, was established. An assumption was made that the whole solar energy supplied to the load would be generated in the optimised system.

Exploring N-Rich Phases in Li(x)N(y) Clusters for Hydrogen Storage at Nanoscale.

PubMed

Bhattacharya, Amrita; Bhattacharya, Saswata

2015-09-17

We have performed cascade genetic algorithm and ab initio atomistic thermodynamics under the framework of first-principles-based hybrid density functional theory to study the (meta-)stability of a wide range of Li(x)N(y) clusters. We found that hybrid xc-functional is essential to address this problem as a local/semilocal functional simply fails even to predict a qualitative prediction. Most importantly, we find that though in bulk lithium nitride, the Li-rich phase, that is, Li3N, is the stable stoichiometry; in small Li(x)N(y) clusters, N-rich phases are more stable at thermodynamic equilibrium. We further show that these N-rich clusters are promising hydrogen storage material because of their easy adsorption and desorption ability at respectively low (≤300 K) and moderately high temperature (≥600 K).

Genetic architecture of male sterility and segregation distortion in Drosophila pseudoobscura Bogota-USA hybrids.

PubMed

Phadnis, Nitin

2011-11-01

Understanding the genetic basis of reproductive isolation between recently diverged species is a central problem in evolutionary genetics. Here, I present analyses of the genetic architecture underlying hybrid male sterility and segregation distortion between the Bogota and USA subspecies of Drosophila pseudoobscura. Previously, a single gene, Overdrive (Ovd), was shown to be necessary but not sufficient for both male sterility and segregation distortion in F(1) hybrids between these subspecies, requiring several interacting partner loci for full manifestation of hybrid phenomena. I map these partner loci separately on the Bogota X chromosome and USA autosomes using a combination of different mapping strategies. I find that hybrid sterility involves a single hybrid incompatibility of at least seven interacting partner genes that includes three large-effect loci. Segregation distortion involves three loci on the Bogota X chromosome and one locus on the autosomes. The genetic bases of hybrid sterility and segregation distortion are at least partially--but not completely--overlapping. My results lay the foundation for fine-mapping experiments to identify the complete set of genes that interact with Overdrive. While individual genes that cause hybrid sterility or inviability have been identified in a few cases, my analysis provides a comprehensive look at the genetic architecture of all components of a hybrid incompatibility underlying F(1) hybrid sterility. Such an analysis would likely be unfeasible for most species pairs due to their divergence time and emphasizes the importance of young species pairs such as the D. pseudoobscura subspecies studied here.

A multiobjective optimization model and an orthogonal design-based hybrid heuristic algorithm for regional urban mining management problems.

PubMed

Wu, Hao; Wan, Zhong

2018-02-01

In this paper, a multiobjective mixed-integer piecewise nonlinear programming model (MOMIPNLP) is built to formulate the management problem of urban mining system, where the decision variables are associated with buy-back pricing, choices of sites, transportation planning, and adjustment of production capacity. Different from the existing approaches, the social negative effect, generated from structural optimization of the recycling system, is minimized in our model, as well as the total recycling profit and utility from environmental improvement are jointly maximized. For solving the problem, the MOMIPNLP model is first transformed into an ordinary mixed-integer nonlinear programming model by variable substitution such that the piecewise feature of the model is removed. Then, based on technique of orthogonal design, a hybrid heuristic algorithm is developed to find an approximate Pareto-optimal solution, where genetic algorithm is used to optimize the structure of search neighborhood, and both local branching algorithm and relaxation-induced neighborhood search algorithm are employed to cut the searching branches and reduce the number of variables in each branch. Numerical experiments indicate that this algorithm spends less CPU (central processing unit) time in solving large-scale regional urban mining management problems, especially in comparison with the similar ones available in literature. By case study and sensitivity analysis, a number of practical managerial implications are revealed from the model. Since the metal stocks in society are reliable overground mineral sources, urban mining has been paid great attention as emerging strategic resources in an era of resource shortage. By mathematical modeling and development of efficient algorithms, this paper provides decision makers with useful suggestions on the optimal design of recycling system in urban mining. For example, this paper can answer how to encourage enterprises to join the recycling activities by government's support and subsidies, whether the existing recycling system can meet the developmental requirements or not, and what is a reasonable adjustment of production capacity.

Novel bio-inspired smart control for hazard mitigation of civil structures

NASA Astrophysics Data System (ADS)

Kim, Yeesock; Kim, Changwon; Langari, Reza

2010-11-01

In this paper, a new bio-inspired controller is proposed for vibration mitigation of smart structures subjected to ground disturbances (i.e. earthquakes). The control system is developed through the integration of a brain emotional learning (BEL) algorithm with a proportional-integral-derivative (PID) controller and a semiactive inversion (Inv) algorithm. The BEL algorithm is based on the neurologically inspired computational model of the amygdala and the orbitofrontal cortex. To demonstrate the effectiveness of the proposed hybrid BEL-PID-Inv control algorithm, a seismically excited building structure equipped with a magnetorheological (MR) damper is investigated. The performance of the proposed hybrid BEL-PID-Inv control algorithm is compared with that of passive, PID, linear quadratic Gaussian (LQG), and BEL control systems. In the simulation, the robustness of the hybrid BEL-PID-Inv control algorithm in the presence of modeling uncertainties as well as external disturbances is investigated. It is shown that the proposed hybrid BEL-PID-Inv control algorithm is effective in improving the dynamic responses of seismically excited building structure-MR damper systems.

Opposition-Based Memetic Algorithm and Hybrid Approach for Sorting Permutations by Reversals.

PubMed

Soncco-Álvarez, José Luis; Muñoz, Daniel M; Ayala-Rincón, Mauricio

2018-02-21

Sorting unsigned permutations by reversals is a difficult problem; indeed, it was proved to be NP-hard by Caprara (1997). Because of its high complexity, many approximation algorithms to compute the minimal reversal distance were proposed until reaching the nowadays best-known theoretical ratio of 1.375. In this article, two memetic algorithms to compute the reversal distance are proposed. The first one uses the technique of opposition-based learning leading to an opposition-based memetic algorithm; the second one improves the previous algorithm by applying the heuristic of two breakpoint elimination leading to a hybrid approach. Several experiments were performed with one-hundred randomly generated permutations, single benchmark permutations, and biological permutations. Results of the experiments showed that the proposed OBMA and Hybrid-OBMA algorithms achieve the best results for practical cases, that is, for permutations of length up to 120. Also, Hybrid-OBMA showed to improve the results of OBMA for permutations greater than or equal to 60. The applicability of our proposed algorithms was checked processing permutations based on biological data, in which case OBMA gave the best average results for all instances.

A Simple Genetic Incompatibility Causes Hybrid Male Sterility in Mimulus

PubMed Central

Sweigart, Andrea L.; Fishman, Lila; Willis, John H.

2006-01-01

Much evidence has shown that postzygotic reproductive isolation (hybrid inviability or sterility) evolves by the accumulation of interlocus incompatibilities between diverging populations. Although in theory only a single pair of incompatible loci is needed to isolate species, empirical work in Drosophila has revealed that hybrid fertility problems often are highly polygenic and complex. In this article we investigate the genetic basis of hybrid sterility between two closely related species of monkeyflower, Mimulus guttatus and M. nasutus. In striking contrast to Drosophila systems, we demonstrate that nearly complete hybrid male sterility in Mimulus results from a simple genetic incompatibility between a single pair of heterospecific loci. We have genetically mapped this sterility effect: the M. guttatus allele at the hybrid male sterility 1 (hms1) locus acts dominantly in combination with recessive M. nasutus alleles at the hybrid male sterility 2 (hms2) locus to cause nearly complete hybrid male sterility. In a preliminary screen to find additional small-effect male sterility factors, we identified one additional locus that also contributes to some of the variation in hybrid male fertility. Interestingly, hms1 and hms2 also cause a significant reduction in hybrid female fertility, suggesting that sex-specific hybrid defects might share a common genetic basis. This possibility is supported by our discovery that recombination is reduced dramatically in a cross involving a parent with the hms1–hms2 incompatibility. PMID:16415357

A simple genetic incompatibility causes hybrid male sterility in mimulus.

PubMed

Sweigart, Andrea L; Fishman, Lila; Willis, John H

2006-04-01

Much evidence has shown that postzygotic reproductive isolation (hybrid inviability or sterility) evolves by the accumulation of interlocus incompatibilities between diverging populations. Although in theory only a single pair of incompatible loci is needed to isolate species, empirical work in Drosophila has revealed that hybrid fertility problems often are highly polygenic and complex. In this article we investigate the genetic basis of hybrid sterility between two closely related species of monkeyflower, Mimulus guttatus and M. nasutus. In striking contrast to Drosophila systems, we demonstrate that nearly complete hybrid male sterility in Mimulus results from a simple genetic incompatibility between a single pair of heterospecific loci. We have genetically mapped this sterility effect: the M. guttatus allele at the hybrid male sterility 1 (hms1) locus acts dominantly in combination with recessive M. nasutus alleles at the hybrid male sterility 2 (hms2) locus to cause nearly complete hybrid male sterility. In a preliminary screen to find additional small-effect male sterility factors, we identified one additional locus that also contributes to some of the variation in hybrid male fertility. Interestingly, hms1 and hms2 also cause a significant reduction in hybrid female fertility, suggesting that sex-specific hybrid defects might share a common genetic basis. This possibility is supported by our discovery that recombination is reduced dramatically in a cross involving a parent with the hms1-hms2 incompatibility.

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles. A Survey

DOE PAGES

Malikopoulos, Andreas

2014-03-31

The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.

Genome-wide mapping in a house mouse hybrid zone reveals hybrid sterility loci and Dobzhansky-Muller interactions

PubMed Central

Turner, Leslie M; Harr, Bettina

2014-01-01

Mapping hybrid defects in contact zones between incipient species can identify genomic regions contributing to reproductive isolation and reveal genetic mechanisms of speciation. The house mouse features a rare combination of sophisticated genetic tools and natural hybrid zones between subspecies. Male hybrids often show reduced fertility, a common reproductive barrier between incipient species. Laboratory crosses have identified sterility loci, but each encompasses hundreds of genes. We map genetic determinants of testis weight and testis gene expression using offspring of mice captured in a hybrid zone between M. musculus musculus and M. m. domesticus. Many generations of admixture enables high-resolution mapping of loci contributing to these sterility-related phenotypes. We identify complex interactions among sterility loci, suggesting multiple, non-independent genetic incompatibilities contribute to barriers to gene flow in the hybrid zone. DOI: http://dx.doi.org/10.7554/eLife.02504.001 PMID:25487987

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Genetic origin and composition of a natural hybrid poplar Populus × jrtyschensis from two distantly related species.

PubMed

Jiang, Dechun; Feng, Jianju; Dong, Miao; Wu, Guili; Mao, Kangshan; Liu, Jianquan

2016-04-18

The factors that contribute to and maintain hybrid zones between distinct species are highly variable, depending on hybrid origins, frequencies and fitness. In this study, we aimed to examine genetic origins, compositions and possible maintenance of Populus × jrtyschensis, an assumed natural hybrid between two distantly related species. This hybrid poplar occurs mainly on the floodplains along the river valleys between the overlapping distributions of the two putative parents. We collected 566 individuals from 45 typical populations of P. × jrtyschensis, P. nigra and P. laurifolia. We genotyped them based on the sequence variations of one maternally inherited chloroplast DNA (cpDNA) fragment and genetic polymorphisms at 20 SSR loci. We further sequenced eight nuclear genes for 168 individuals from 31 populations. Two groups of cpDNA haplotypes characteristic of P. nigra and P. laurifolia respectively were both recovered for P. × jrtyschensis. Genetic structures and coalescent tests of two sets of nuclear population genetic data suggested that P. × jrtyschensis originated from hybridizations between the two assumed parental species. All examined populations of P. × jrtyschensis comprise mainly F1 hybrids from interspecific hybridizations between P. nigra and P. laurifolia. In the habitats of P. × jrtyschensis, there are lower concentrations of soil nitrogen than in the habitats occupied by the other two species. Our extensive examination of the genetic composition of P. × jrtyschensis suggested that it is typical of F1-dominated hybrid zones. This finding plus the low concentration of soil nitrogen in the floodplain soils support the F1-dominated bounded hybrid superiority hypothesis of hybrid zone maintenance for this particular hybrid poplar.

GALAXY: A new hybrid MOEA for the optimal design of Water Distribution Systems

NASA Astrophysics Data System (ADS)

Wang, Q.; Savić, D. A.; Kapelan, Z.

2017-03-01

A new hybrid optimizer, called genetically adaptive leaping algorithm for approximation and diversity (GALAXY), is proposed for dealing with the discrete, combinatorial, multiobjective design of Water Distribution Systems (WDSs), which is NP-hard and computationally intensive. The merit of GALAXY is its ability to alleviate to a great extent the parameterization issue and the high computational overhead. It follows the generational framework of Multiobjective Evolutionary Algorithms (MOEAs) and includes six search operators and several important strategies. These operators are selected based on their leaping ability in the objective space from the global and local search perspectives. These strategies steer the optimization and balance the exploration and exploitation aspects simultaneously. A highlighted feature of GALAXY lies in the fact that it eliminates majority of parameters, thus being robust and easy-to-use. The comparative studies between GALAXY and three representative MOEAs on five benchmark WDS design problems confirm its competitiveness. GALAXY can identify better converged and distributed boundary solutions efficiently and consistently, indicating a much more balanced capability between the global and local search. Moreover, its advantages over other MOEAs become more substantial as the complexity of the design problem increases.

Manifold absolute pressure estimation using neural network with hybrid training algorithm

PubMed Central

Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

2017-01-01

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value. PMID:29190779

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.

PubMed

Zafar, Mohd; Van Vinh, N; Behera, Shishir Kumar; Park, Hung-Suck

2017-04-01

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box-Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand-metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model-genetic algorithm (RSM-GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47μg/g) is facilitated at 30.22mg C/L of EtOH with initial As(III) concentration of 196.77μg/L at pH5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM. Copyright © 2016. Published by Elsevier B.V.

Stabilization process in Saccharomyces intra and interspecific hybrids in fermentative conditions.

PubMed

Pérez-Través, Laura; Lopes, Christian A; Barrio, Eladio; Querol, Amparo

2014-12-01

We evaluated the genetic stabilization of artificial intra- (Saccharomyces cerevisiae) and interspecific (S. cerevisiae × S. kudriavzevii) hybrids under wine fermentative conditions. Large-scale transitions in genome size and genome reorganizations were observed during this process. Interspecific hybrids seem to need fewer generations to reach genetic stability than intraspecific hybrids. The largest number of molecular patterns recovered among the derived clones was observed for intraspecific hybrids, particularly for those obtained by rare-mating. Molecular marker analyses revealed that unstable clones could change during the industrial process to obtain active dry yeast. When no changes in molecular markers and ploidy were observed after this process, no changes in genetic composition were confirmed by comparative genome hybridization, considering the clone as a stable hybrid. According to our results, under these conditions, fermentation steps 3 and 5 (30-50 generations) would suffice to obtain genetically stable interspecific and intraspecific hybrids, respectively. Copyright© by the Spanish Society for Microbiology and Institute for Catalan Studies.

A hybrid CS-SA intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demands

NASA Astrophysics Data System (ADS)

Moslemipour, Ghorbanali

2018-07-01

This paper aims at proposing a quadratic assignment-based mathematical model to deal with the stochastic dynamic facility layout problem. In this problem, product demands are assumed to be dependent normally distributed random variables with known probability density function and covariance that change from period to period at random. To solve the proposed model, a novel hybrid intelligent algorithm is proposed by combining the simulated annealing and clonal selection algorithms. The proposed model and the hybrid algorithm are verified and validated using design of experiment and benchmark methods. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view. Besides, the proposed model can be used in both of the stochastic and deterministic situations.

A Benders based rolling horizon algorithm for a dynamic facility location problem

DOE PAGES

Marufuzzaman,, Mohammad; Gedik, Ridvan; Roni, Mohammad S.

2016-06-28

This study presents a well-known capacitated dynamic facility location problem (DFLP) that satisfies the customer demand at a minimum cost by determining the time period for opening, closing, or retaining an existing facility in a given location. To solve this challenging NP-hard problem, this paper develops a unique hybrid solution algorithm that combines a rolling horizon algorithm with an accelerated Benders decomposition algorithm. Extensive computational experiments are performed on benchmark test instances to evaluate the hybrid algorithm’s efficiency and robustness in solving the DFLP problem. Computational results indicate that the hybrid Benders based rolling horizon algorithm consistently offers high qualitymore » feasible solutions in a much shorter computational time period than the standalone rolling horizon and accelerated Benders decomposition algorithms in the experimental range.« less

Genetic and morphological structure of a spruce hybrid (Picea sitchensis x P. glauca) zone along a climatic gradient.

PubMed

Hamilton, Jill A; Aitken, Sally N

2013-08-01

Historic colonization and contemporary evolutionary processes contribute to patterns of genetic variation and differentiation among populations. However, separating the respective influences of these processes remains a challenge, particularly for natural hybrid zones, where standing genetic variation may result from evolutionary processes both preceding and following contact, influencing the evolutionary trajectory of hybrid populations. Where adaptation to novel environments may be facilitated by interspecific hybridization, teasing apart these processes will have practical implications for forest management in changing environments. We evaluated the neutral genetic architecture of the Picea sitchensis (Sitka spruce) × P. glauca (white spruce) hybrid zone along the Nass and Skeena river valleys in northwestern British Columbia using chloroplast, mitochondrial, and nuclear microsatellite markers, in combination with cone morphological traits. Sitka spruce mitotype "capture", evidenced by this species dominating the maternal lineage, is consistent with earlier colonization of the region by Sitka spruce. This "capture" differs from the spatial distribution of chloroplast haplotypes, indicating pollen dispersal and its contribution to geographic structure. Genetic ancestry, based on nuclear markers, was strongly influenced by climate and geography. Highly parallel results for replicate transects along environmental gradients provide support for the bounded hybrid superiority model of hybrid zone maintenance. • This broad-scale analysis of neutral genetic structure indicates the importance of historic and contemporary gene flow, environmental selection, and their interaction in shaping neutral genetic variation within this hybrid zone, informative to seed transfer development and reforestation for future climates.

Genetic basis of hybrid male sterility among three closely related species of Drosophila.

PubMed

Mishra, Paras Kumar; Singh, B N

2005-05-01

The genetic basis of hybrid male sterility among three closely related species, Drosophila bipectinata, D. parabipectinata and D. malerkotliana has been investigated by using backcross analysis methods. The role of Y chromosome, major hybrid sterility (MHS) genes (genetic factors) and cytoplasm (non-genetic factor) have been studied in the hybrids of these three species. In the species pair, bipectinata--parabipectinata, Y chromosome introgression of parabipectinata in the genomic background of bipectinata and the reciprocal Y chromosome introgression were unsuccessful as all males in second backcross generation were sterile. Neither MHS genes nor cytoplasm was found important for sterility. This suggests the involvement of X-Y, X-autosomes or polygenic interactions in hybrid male sterility. In bipectinata--malerkotliana and parabipectinata--malerkotliana species pairs, Y chromosome substitution in reciprocal crosses did not affect male fertility. Backcross analyses also show no involvement of MHS genes or cytoplasm in hybrid male sterility in these two species pairs. Therefore, X- autosome interaction or polygenic interaction is supposed to be involved in hybrid male sterility in these two species pairs. These findings also provide evidence that even in closely related species, genetic interactions underlying hybrid male sterility may vary.

A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Genrang; Lin, ZhengChun

The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

Computing all hybridization networks for multiple binary phylogenetic input trees.

PubMed

Albrecht, Benjamin

2015-07-30

The computation of phylogenetic trees on the same set of species that are based on different orthologous genes can lead to incongruent trees. One possible explanation for this behavior are interspecific hybridization events recombining genes of different species. An important approach to analyze such events is the computation of hybridization networks. This work presents the first algorithm computing the hybridization number as well as a set of representative hybridization networks for multiple binary phylogenetic input trees on the same set of taxa. To improve its practical runtime, we show how this algorithm can be parallelized. Moreover, we demonstrate the efficiency of the software Hybroscale, containing an implementation of our algorithm, by comparing it to PIRNv2.0, which is so far the best available software computing the exact hybridization number for multiple binary phylogenetic trees on the same set of taxa. The algorithm is part of the software Hybroscale, which was developed specifically for the investigation of hybridization networks including their computation and visualization. Hybroscale is freely available(1) and runs on all three major operating systems. Our simulation study indicates that our approach is on average 100 times faster than PIRNv2.0. Moreover, we show how Hybroscale improves the interpretation of the reported hybridization networks by adding certain features to its graphical representation.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

75 FR 36147 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Order Approving...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-24

..., as Modified by Amendment No. 1 Thereto, Related to the Hybrid Matching Algorithms June 17, 2010. On... Hybrid System. Each rule currently provides allocation algorithms the Exchange can utilize when executing incoming electronic orders, including the Ultimate Matching Algorithm (``UMA''), and price-time and pro...

Fish based preimplantation genetic diagnosis to prevent DiGeorge syndrome.

PubMed

Shefi, Shai; Raviv, Gil; Rienstein, Shlomit; Barkai, Gad; Aviram-Goldring, Ayala; Levron, Jacob

2009-07-01

To report the performance of fluorescence in-situ hybridization in the setting of preimplantation genetic diagnosis in order to diagnose embryos affected by DiGeorge syndrome. Case report. Academic referral center. A 32 year-old female affected by DiGeorge syndrome. History and physical examination, karyotyping, amniocentesis, preimplantation genetic diagnosis, fluorescence in-situ hybridization. Avoidance of pregnancy with embryo affected by DiGeorge syndrome. Termination of pregnancy with an affected embryo followed by fluorescence in-situ hybridization based preimplantation genetic diagnosis and delivery of healthy offspring. The combination of preimplantation genetic diagnosis with fluorescence in-situ hybridization is recommended to prevent pregnancies with DiGeorge syndrome affected embryos in properly selected patients.

A Novel Hybrid Firefly Algorithm for Global Optimization.

PubMed

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

A Novel Hybrid Firefly Algorithm for Global Optimization

PubMed Central

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

2016-01-01

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869

Genetic, morphological, and spectral characterization of relictual Niobrara River hybrid aspens (Populus × smithii).

PubMed

Deacon, Nicholas John; Grossman, Jake Joseph; Schweiger, Anna Katharina; Armour, Isabella; Cavender-Bares, Jeannine

2017-12-01

Aspen groves along the Niobrara River in Nebraska have long been a biogeographic curiosity due to morphological differences from nearby remnant Populus tremuloides populations. Pleistocene hybridization between P. tremuloides and P. grandidentata has been proposed, but the nearest P. grandidentata populations are currently several hundred kilometers east. We tested the hybrid-origin hypothesis using genetic data and characterized putative hybrids phenotypically. We compared nuclear microsatellite loci and chloroplast sequences of Niobrara River aspens to their putative parental species. Parental species and putative hybrids were also grown in a common garden for phenotypic comparison. On the common garden plants, we measured leaf morphological traits and leaf-level spectral reflectance profiles, from which chemical traits were derived. The genetic composition of the three unique Niobrara aspen genotypes is consistent with the hybridization hypothesis and with maternal chloroplast inheritance from P. grandidentata . Leaf margin dentition and abaxial pubescence differentiated taxa, with the hybrids showing intermediate values. Spectral profiles allowed statistical separation of taxa in short-wave infrared wavelengths, with hybrids showing intermediate values, indicating that traits associated with internal structure of leaves and water absorption may vary among taxa. However, reflectance values in the visible region did not differentiate taxa, indicating that traits related to pigments are not differentiated. Both genetic and phenotypic results support the hypothesis of a hybrid origin for these genetically unique aspens. However, low genetic diversity and ongoing ecological and climatic threats to the hybrid taxon present a challenge for conservation of these relictual boreal communities. © 2017 Botanical Society of America.

Genetic Architecture of Male Sterility and Segregation Distortion in Drosophila pseudoobscura Bogota–USA Hybrids

PubMed Central

Phadnis, Nitin

2011-01-01

Understanding the genetic basis of reproductive isolation between recently diverged species is a central problem in evolutionary genetics. Here, I present analyses of the genetic architecture underlying hybrid male sterility and segregation distortion between the Bogota and USA subspecies of Drosophila pseudoobscura. Previously, a single gene, Overdrive (Ovd), was shown to be necessary but not sufficient for both male sterility and segregation distortion in F1 hybrids between these subspecies, requiring several interacting partner loci for full manifestation of hybrid phenomena. I map these partner loci separately on the Bogota X chromosome and USA autosomes using a combination of different mapping strategies. I find that hybrid sterility involves a single hybrid incompatibility of at least seven interacting partner genes that includes three large-effect loci. Segregation distortion involves three loci on the Bogota X chromosome and one locus on the autosomes. The genetic bases of hybrid sterility and segregation distortion are at least partially—but not completely—overlapping. My results lay the foundation for fine-mapping experiments to identify the complete set of genes that interact with Overdrive. While individual genes that cause hybrid sterility or inviability have been identified in a few cases, my analysis provides a comprehensive look at the genetic architecture of all components of a hybrid incompatibility underlying F1 hybrid sterility. Such an analysis would likely be unfeasible for most species pairs due to their divergence time and emphasizes the importance of young species pairs such as the D. pseudoobscura subspecies studied here. PMID:21900263

Optimizing Constrained Single Period Problem under Random Fuzzy Demand

NASA Astrophysics Data System (ADS)

Taleizadeh, Ata Allah; Shavandi, Hassan; Riazi, Afshin

2008-09-01

In this paper, we consider the multi-product multi-constraint newsboy problem with random fuzzy demands and total discount. The demand of the products is often stochastic in the real word but the estimation of the parameters of distribution function may be done by fuzzy manner. So an appropriate option to modeling the demand of products is using the random fuzzy variable. The objective function of proposed model is to maximize the expected profit of newsboy. We consider the constraints such as warehouse space and restriction on quantity order for products, and restriction on budget. We also consider the batch size for products order. Finally we introduce a random fuzzy multi-product multi-constraint newsboy problem (RFM-PM-CNP) and it is changed to a multi-objective mixed integer nonlinear programming model. Furthermore, a hybrid intelligent algorithm based on genetic algorithm, Pareto and TOPSIS is presented for the developed model. Finally an illustrative example is presented to show the performance of the developed model and algorithm.

Different types of maximum power point tracking techniques for renewable energy systems: A survey

NASA Astrophysics Data System (ADS)

Khan, Mohammad Junaid; Shukla, Praveen; Mustafa, Rashid; Chatterji, S.; Mathew, Lini

2016-03-01

Global demand for electricity is increasing while production of energy from fossil fuels is declining and therefore the obvious choice of the clean energy source that is abundant and could provide security for development future is energy from the sun. In this paper, the characteristic of the supply voltage of the photovoltaic generator is nonlinear and exhibits multiple peaks, including many local peaks and a global peak in non-uniform irradiance. To keep global peak, MPPT is the important component of photovoltaic systems. Although many review articles discussed conventional techniques such as P & O, incremental conductance, the correlation ripple control and very few attempts have been made with intelligent MPPT techniques. This document also discusses different algorithms based on fuzzy logic, Ant Colony Optimization, Genetic Algorithm, artificial neural networks, Particle Swarm Optimization Algorithm Firefly, Extremum seeking control method and hybrid methods applied to the monitoring of maximum value of power at point in systems of photovoltaic under changing conditions of irradiance.

Genetic algorithm prediction of two-dimensional group-IV dioxides for dielectrics

NASA Astrophysics Data System (ADS)

Singh, Arunima K.; Revard, Benjamin C.; Ramanathan, Rohit; Ashton, Michael; Tavazza, Francesca; Hennig, Richard G.

2017-04-01

Two-dimensional (2D) materials present a new class of materials whose structures and properties can differ from their bulk counterparts. We perform a genetic algorithm structure search using density-functional theory to identify low-energy structures of 2D group-IV dioxides A O2 (A =Si , Ge, Sn, Pb). We find that 2D SiO2 is most stable in the experimentally determined bi-tetrahedral structure, while 2D SnO2 and PbO2 are most stable in the 1 T structure. For 2D GeO2, the genetic algorithm finds a new low-energy 2D structure with monoclinic symmetry. Each system exhibits 2D structures with formation energies ranging from 26 to 151 meV/atom, below those of certain already synthesized 2D materials. The phonon spectra confirm their dynamic stability. Using the HSE06 hybrid functional, we determine that the 2D dioxides are insulators or semiconductors, with a direct band gap of 7.2 eV at Γ for 2D SiO2, and indirect band gaps of 4.8-2.7 eV for the other dioxides. To guide future applications of these 2D materials in nanoelectronic devices, we determine their band-edge alignment with graphene, phosphorene, and single-layer BN and MoS2. An assessment of the dielectric properties and electrochemical stability of the 2D group-IV dioxides shows that 2D GeO2 and SnO2 are particularly promising candidates for gate oxides and 2D SnO2 also as a protective layer in heterostructure nanoelectronic devices.

Hybrid real-code ant colony optimisation for constrained mechanical design

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Bureerat, Sujin

2016-01-01

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

Evolution of cellular automata with memory: The Density Classification Task.

PubMed

Stone, Christopher; Bull, Larry

2009-08-01

The Density Classification Task is a well known test problem for two-state discrete dynamical systems. For many years researchers have used a variety of evolutionary computation approaches to evolve solutions to this problem. In this paper, we investigate the evolvability of solutions when the underlying Cellular Automaton is augmented with a type of memory based on the Least Mean Square algorithm. To obtain high performance solutions using a simple non-hybrid genetic algorithm, we design a novel representation based on the ternary representation used for Learning Classifier Systems. The new representation is found able to produce superior performance to the bit string traditionally used for representing Cellular automata. Moreover, memory is shown to improve evolvability of solutions and appropriate memory settings are able to be evolved as a component part of these solutions.

A genomic approach to identify hybrid incompatibility genes.

PubMed

Cooper, Jacob C; Phadnis, Nitin

2016-07-02

Uncovering the genetic and molecular basis of barriers to gene flow between populations is key to understanding how new species are born. Intrinsic postzygotic reproductive barriers such as hybrid sterility and hybrid inviability are caused by deleterious genetic interactions known as hybrid incompatibilities. The difficulty in identifying these hybrid incompatibility genes remains a rate-limiting step in our understanding of the molecular basis of speciation. We recently described how whole genome sequencing can be applied to identify hybrid incompatibility genes, even from genetically terminal hybrids. Using this approach, we discovered a new hybrid incompatibility gene, gfzf, between Drosophila melanogaster and Drosophila simulans, and found that it plays an essential role in cell cycle regulation. Here, we discuss the history of the hunt for incompatibility genes between these species, discuss the molecular roles of gfzf in cell cycle regulation, and explore how intragenomic conflict drives the evolution of fundamental cellular mechanisms that lead to the developmental arrest of hybrids.

A class of multi-period semi-variance portfolio for petroleum exploration and development

NASA Astrophysics Data System (ADS)

Guo, Qiulin; Li, Jianzhong; Zou, Caineng; Guo, Yujuan; Yan, Wei

2012-10-01

Variance is substituted by semi-variance in Markowitz's portfolio selection model. For dynamic valuation on exploration and development projects, one period portfolio selection is extended to multi-period. In this article, a class of multi-period semi-variance exploration and development portfolio model is formulated originally. Besides, a hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the mode is effective and feasible.

Design and control strategy for a hybrid green energy system for mobile telecommunication sites

NASA Astrophysics Data System (ADS)

Okundamiya, Michael S.; Emagbetere, Joy O.; Ogujor, Emmanuel A.

2014-07-01

The rising energy costs and carbon footprint of operating mobile telecommunication sites in the emerging world have increased research interests in green technology. The intermittent nature of most green energy sources creates the problem of designing the optimum configuration for a given location. This study presents the design analysis and control strategy for a cost effective and reliable operation of the hybrid green energy system (HGES) for GSM base transceiver station (BTS) sites in isolated regions. The design constrains the generation and distribution of power to reliably satisfy the energy demand while ensuring safe operation of the system. The overall process control applies the genetic algorithm-based technique for optimal techno-economic sizing of system's components. The process simulation utilized meteorological data for 3 locations (Abuja, Benin City and Sokoto) with varying climatic conditions in Nigeria. Simulation results presented for green GSM BTS sites are discussed and compared with existing approaches.

A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

NASA Astrophysics Data System (ADS)

Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

2017-08-01

The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

Natural hybridization and genetic and morphological variation between two epiphytic bromeliads

PubMed Central

Neri, Jordana; Wendt, Tânia

2018-01-01

Abstract Reproductive isolation is of fundamental importance for maintaining species boundaries in sympatry. Here, we examine the genetic and morphological differences between two closely related bromeliad species: Vriesea simplex and Vriesea scalaris. Furthermore, we examined the occurrence of natural hybridization and discuss the action of reproductive isolation barriers. Nuclear genomic admixture suggests hybridization in sympatric populations, although interspecific gene flow is low among species in all sympatric zones (Nem < 0.5). Thus, morphological and genetic divergence (10.99 %) between species can be maintained despite ongoing natural hybridization. Cross-evaluation of our genetic and morphological data suggests that species integrity is maintained by the simultaneous action of multiple barriers, such as divergent reproductive systems among species, differences in floral traits and low hybrid seed viability. PMID:29308124

Hybrid Cryptosystem Using Tiny Encryption Algorithm and LUC Algorithm

NASA Astrophysics Data System (ADS)

Rachmawati, Dian; Sharif, Amer; Jaysilen; Andri Budiman, Mohammad

2018-01-01

Security becomes a very important issue in data transmission and there are so many methods to make files more secure. One of that method is cryptography. Cryptography is a method to secure file by writing the hidden code to cover the original file. Therefore, if the people do not involve in cryptography, they cannot decrypt the hidden code to read the original file. There are many methods are used in cryptography, one of that method is hybrid cryptosystem. A hybrid cryptosystem is a method that uses a symmetric algorithm to secure the file and use an asymmetric algorithm to secure the symmetric algorithm key. In this research, TEA algorithm is used as symmetric algorithm and LUC algorithm is used as an asymmetric algorithm. The system is tested by encrypting and decrypting the file by using TEA algorithm and using LUC algorithm to encrypt and decrypt the TEA key. The result of this research is by using TEA Algorithm to encrypt the file, the cipher text form is the character from ASCII (American Standard for Information Interchange) table in the form of hexadecimal numbers and the cipher text size increase by sixteen bytes as the plaintext length is increased by eight characters.

Genetic divergence among corn hybrids and combining ability for agronomic and bromatological traits of silage.

PubMed

Gralak, E; Faria, M V; Figueiredo, A S T; Rizzardi, D A; Neumann, M; Mendes, M C; Scapim, C A; Galbeiro, S

2017-05-25

We assessed the impact of genetic divergence and the ability to combine corn hybrids used for the production of silage on the agronomic and bromatological traits of silage quality. We evaluated 18 corn hybrids used as genitors in a circulant diallel scheme in which each genitor hybrid participated in 9 hybrid combinations, and evaluated 100 treatments [18 genitor hybrids, 81 diallelic hybrids, and a commercial check hybrid (DKB330)] in a triple lattice 10 x 10 experimental design in two environments in Brazil. Genetic variability was adequate among the corn silage hybrids, and we can recommend the use of genitors 2B688 and P30B39 for the formation of a base population for intrapopulational breeding. The P30P34 hybrid is the best for intrapopulational breeding when aiming for silage with high protein content, low fiber content, and higher in vitro digestibility. Interpopulational breeding directed at improving silage digestibility can use a combination of genitors P30P34 and AS1572, but AS1572 and P30K64 are the most recommended. Hybrids 2B688, P30P34, and SG6015 are considered the most genetically distant of the others hybrids, and have desirable combining potential; therefore, they are important genitors for the formation of new segregated populations for improving corn silage.

Problem solving with genetic algorithms and Splicer

NASA Technical Reports Server (NTRS)

Bayer, Steven E.; Wang, Lui

1991-01-01

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

PubMed Central

2010-01-01

Background Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. Results PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. Conclusions The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics. PMID:20825661

Epigenetic patterns newly established after interspecific hybridization in natural populations of Solanum

PubMed Central

Cara, Nicolás; Marfil, Carlos F; Masuelli, Ricardo W

2013-01-01

Interspecific hybridization is known for triggering genetic and epigenetic changes, such as modifications on DNA methylation patterns and impact on phenotypic plasticity and ecological adaptation. Wild potatoes (Solanum, section Petota) are adapted to multiple habitats along the Andes, and natural hybridizations have proven to be a common feature among species of this group. Solanum × rechei, a recently formed hybrid that grows sympatrically with the parental species S. kurtzianum and S. microdontum, represents an ideal model for studying the ecologically and evolutionary importance of hybridization in generating of epigenetic variability. Genetic and epigenetic variability and their correlation with morphological variation were investigated in wild and ex situ conserved populations of these three wild potato species using amplified fragment length polymorphism (AFLP) and methylation-sensitive amplified polymorphism (MSAP) techniques. We observed that novel methylation patterns doubled the number of novel genetic patterns in the hybrid and that the morphological variability measured on 30 characters had a higher correlation with the epigenetic than with the genetic variability. Statistical comparison of methylation levels suggested that the interspecific hybridization induces genome demethylation in the hybrids. A Bayesian analysis of the genetic data reveled the hybrid nature of S. × rechei, with genotypes displaying high levels of admixture with the parental species, while the epigenetic information assigned S. × rechei to its own cluster with low admixture. These findings suggested that after the hybridization event, a novel epigenetic pattern was rapidly established, which might influence the phenotypic plasticity and adaptation of the hybrid to new environments. PMID:24198938

"Idiopathic" mental retardation and new chromosomal abnormalities

PubMed Central

2010-01-01

Mental retardation is a heterogeneous condition, affecting 1-3% of general population. In the last few years, several emerging clinical entities have been described, due to the advent of newest genetic techniques, such as array Comparative Genomic Hybridization. The detection of cryptic microdeletion/microduplication abnormalities has allowed genotype-phenotype correlations, delineating recognizable syndromic conditions that are herein reviewed. With the aim to provide to Paediatricians a combined clinical and genetic approach to the child with cognitive impairment, a practical diagnostic algorithm is also illustrated. The use of microarray platforms has further reduced the percentage of "idiopathic" forms of mental retardation, previously accounted for about half of total cases. We discussed the putative pathways at the basis of remaining "pure idiopathic" forms of mental retardation, highlighting possible environmental and epigenetic mechanisms as causes of altered cognition. PMID:20152051

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa; Gholami, Amin

2015-06-01

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.

Genetic algorithms using SISAL parallel programming language

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tejada, S.

1994-05-06

Genetic algorithms are a mathematical optimization technique developed by John Holland at the University of Michigan [1]. The SISAL programming language possesses many of the characteristics desired to implement genetic algorithms. SISAL is a deterministic, functional programming language which is inherently parallel. Because SISAL is functional and based on mathematical concepts, genetic algorithms can be efficiently translated into the language. Several of the steps involved in genetic algorithms, such as mutation, crossover, and fitness evaluation, can be parallelized using SISAL. In this paper I will l discuss the implementation and performance of parallel genetic algorithms in SISAL.

The role of hybridization in facilitating tree invasion

PubMed Central

2017-01-01

Abstract Hybridization events can generate additional genetic diversity upon which natural selection can act and at times enhance invasiveness of the species. Invasive tree species are a growing ecological concern worldwide, and some of these invasions involve hybridization events pre- or post-introduction. There are 20 hybrid invasive tree taxa in 15 genera (11 plant families) discussed here. When reported, abundance of hybrids comprised 10–100 % of an invasion, the remainder being parental taxa. In seven hybrid taxa, researchers identified phenotypes that may make hybrids better invaders. Twelve hybrid tree taxa involved introgression and more hybrids involved all non-native taxa than native × non-native taxa. Three hybrid tree taxa were the result of intentional crosses, and all hybrid taxa involved intentional introduction of either one or more parental taxon or the hybrid itself. The knowledge gaps present in some hybrid tree taxa can weaken our effectiveness in predicting and controlling invasions, as hybrids can add a level of complexity to an invasion by being morphologically cryptic, causing genetic pollution of a native parental taxon, presenting novel genotypes for which there may not be coevolved biological control agents, or evolving adaptive traits through increased genetic variation. PMID:28028055

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

A hybrid monkey search algorithm for clustering analysis.

PubMed

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

Automated design of infrared digital metamaterials by genetic algorithm

NASA Astrophysics Data System (ADS)

Sugino, Yuya; Ishikawa, Atsushi; Hayashi, Yasuhiko; Tsuruta, Kenji

2017-08-01

We demonstrate automatic design of infrared (IR) metamaterials using a genetic algorithm (GA) and experimentally characterize their IR properties. To implement the automated design scheme of the metamaterial structures, we adopt a digital metamaterial consisting of 7 × 7 Au nano-pixels with an area of 200 nm × 200 nm, and their placements are coded as binary genes in the GA optimization process. The GA combined with three-dimensional (3D) finite element method (FEM) simulation is developed and applied to automatically construct a digital metamaterial to exhibit pronounced plasmonic resonances at the target IR frequencies. Based on the numerical results, the metamaterials are fabricated on a Si substrate over an area of 1 mm × 1 mm by using an EB lithography, Cr/Au (2/20 nm) depositions, and liftoff process. In the FT-IR measurement, pronounced plasmonic responses of each metamaterial are clearly observed near the targeted frequencies, although the synthesized pixel arrangements of the metamaterials are seemingly random. The corresponding numerical simulations reveal the important resonant behavior of each pixel and their hybridized systems. Our approach is fully computer-aided without artificial manipulation, thus paving the way toward the novel device design for next-generation plasmonic device applications.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

PubMed

Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

2016-01-01

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

Hybrid employment recommendation algorithm based on Spark

NASA Astrophysics Data System (ADS)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

NASA Astrophysics Data System (ADS)

Zhan, Liwei; Li, Chengwei

2017-02-01

A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

Climate-Driven Reshuffling of Species and Genes: Potential Conservation Roles for Species Translocations and Recombinant Hybrid Genotypes

PubMed Central

Scriber, Jon Mark

2013-01-01

Comprising 50%–75% of the world’s fauna, insects are a prominent part of biodiversity in communities and ecosystems globally. Biodiversity across all levels of biological classifications is fundamentally based on genetic diversity. However, the integration of genomics and phylogenetics into conservation management may not be as rapid as climate change. The genetics of hybrid introgression as a source of novel variation for ecological divergence and evolutionary speciation (and resilience) may generate adaptive potential and diversity fast enough to respond to locally-altered environmental conditions. Major plant and herbivore hybrid zones with associated communities deserve conservation consideration. This review addresses functional genetics across multi-trophic-level interactions including “invasive species” in various ecosystems as they may become disrupted in different ways by rapid climate change. “Invasive genes” (into new species and populations) need to be recognized for their positive creative potential and addressed in conservation programs. “Genetic rescue” via hybrid translocations may provide needed adaptive flexibility for rapid adaptation to environmental change. While concerns persist for some conservationists, this review emphasizes the positive aspects of hybrids and hybridization. Specific implications of natural genetic introgression are addressed with a few examples from butterflies, including transgressive phenotypes and climate-driven homoploid recombinant hybrid speciation. Some specific examples illustrate these points using the swallowtail butterflies (Papilionidae) with their long-term historical data base (phylogeographical diversity changes) and recent (3-decade) climate-driven temporal and genetic divergence in recombinant homoploid hybrids and relatively recent hybrid speciation of Papilio appalachiensis in North America. Climate-induced “reshuffling” (recombinations) of species composition, genotypes, and genomes may become increasingly ecologically and evolutionarily predictable, but future conservation management programs are more likely to remain constrained by human behavior than by lack of academic knowledge. PMID:26462579

Climate-Driven Reshuffling of Species and Genes: Potential Conservation Roles for Species Translocations and Recombinant Hybrid Genotypes.

PubMed

Scriber, Jon Mark

2013-12-24

Comprising 50%-75% of the world's fauna, insects are a prominent part of biodiversity in communities and ecosystems globally. Biodiversity across all levels of biological classifications is fundamentally based on genetic diversity. However, the integration of genomics and phylogenetics into conservation management may not be as rapid as climate change. The genetics of hybrid introgression as a source of novel variation for ecological divergence and evolutionary speciation (and resilience) may generate adaptive potential and diversity fast enough to respond to locally-altered environmental conditions. Major plant and herbivore hybrid zones with associated communities deserve conservation consideration. This review addresses functional genetics across multi-trophic-level interactions including "invasive species" in various ecosystems as they may become disrupted in different ways by rapid climate change. "Invasive genes" (into new species and populations) need to be recognized for their positive creative potential and addressed in conservation programs. "Genetic rescue" via hybrid translocations may provide needed adaptive flexibility for rapid adaptation to environmental change. While concerns persist for some conservationists, this review emphasizes the positive aspects of hybrids and hybridization. Specific implications of natural genetic introgression are addressed with a few examples from butterflies, including transgressive phenotypes and climate-driven homoploid recombinant hybrid speciation. Some specific examples illustrate these points using the swallowtail butterflies (Papilionidae) with their long-term historical data base (phylogeographical diversity changes) and recent (3-decade) climate-driven temporal and genetic divergence in recombinant homoploid hybrids and relatively recent hybrid speciation of Papilio appalachiensis in North America. Climate-induced "reshuffling" (recombinations) of species composition, genotypes, and genomes may become increasingly ecologically and evolutionarily predictable, but future conservation management programs are more likely to remain constrained by human behavior than by lack of academic knowledge.

A genomic approach to identify hybrid incompatibility genes

PubMed Central

Cooper, Jacob C.; Phadnis, Nitin

2016-01-01

ABSTRACT Uncovering the genetic and molecular basis of barriers to gene flow between populations is key to understanding how new species are born. Intrinsic postzygotic reproductive barriers such as hybrid sterility and hybrid inviability are caused by deleterious genetic interactions known as hybrid incompatibilities. The difficulty in identifying these hybrid incompatibility genes remains a rate-limiting step in our understanding of the molecular basis of speciation. We recently described how whole genome sequencing can be applied to identify hybrid incompatibility genes, even from genetically terminal hybrids. Using this approach, we discovered a new hybrid incompatibility gene, gfzf, between Drosophila melanogaster and Drosophila simulans, and found that it plays an essential role in cell cycle regulation. Here, we discuss the history of the hunt for incompatibility genes between these species, discuss the molecular roles of gfzf in cell cycle regulation, and explore how intragenomic conflict drives the evolution of fundamental cellular mechanisms that lead to the developmental arrest of hybrids. PMID:27230814

Parameter Estimation for a Hybrid Adaptive Flight Controller

NASA Technical Reports Server (NTRS)

Campbell, Stefan F.; Nguyen, Nhan T.; Kaneshige, John; Krishnakumar, Kalmanje

2009-01-01

This paper expands on the hybrid control architecture developed at the NASA Ames Research Center by addressing issues related to indirect adaptation using the recursive least squares (RLS) algorithm. Specifically, the hybrid control architecture is an adaptive flight controller that features both direct and indirect adaptation techniques. This paper will focus almost exclusively on the modifications necessary to achieve quality indirect adaptive control. Additionally this paper will present results that, using a full non -linear aircraft model, demonstrate the effectiveness of the hybrid control architecture given drastic changes in an aircraft s dynamics. Throughout the development of this topic, a thorough discussion of the RLS algorithm as a system identification technique will be provided along with results from seven well-known modifications to the popular RLS algorithm.

Battery algorithm verification and development using hardware-in-the-loop testing

NASA Astrophysics Data System (ADS)

He, Yongsheng; Liu, Wei; Koch, Brain J.

Battery algorithms play a vital role in hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), extended-range electric vehicles (EREVs), and electric vehicles (EVs). The energy management of hybrid and electric propulsion systems needs to rely on accurate information on the state of the battery in order to determine the optimal electric drive without abusing the battery. In this study, a cell-level hardware-in-the-loop (HIL) system is used to verify and develop state of charge (SOC) and power capability predictions of embedded battery algorithms for various vehicle applications. Two different batteries were selected as representative examples to illustrate the battery algorithm verification and development procedure. One is a lithium-ion battery with a conventional metal oxide cathode, which is a power battery for HEV applications. The other is a lithium-ion battery with an iron phosphate (LiFePO 4) cathode, which is an energy battery for applications in PHEVs, EREVs, and EVs. The battery cell HIL testing provided valuable data and critical guidance to evaluate the accuracy of the developed battery algorithms, to accelerate battery algorithm future development and improvement, and to reduce hybrid/electric vehicle system development time and costs.

Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Zhanyong; Lu, Feng; He, Hong-di; Lu, Qing-Chang; Wang, Dongsheng; Peng, Zhong-Ren

2015-03-01

At road intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly in the green-light period, which generates higher velocity fluctuation and thus higher emission rates. Additionally, the frequent changes of wind direction further add the highly variable dispersion of pollutants at the street scale. It is, therefore, very difficult to estimate the distribution of pollutant concentrations using conventional deterministic causal models. For this reason, a hybrid model combining wavelet neural network and genetic algorithm (GA-WNN) is proposed for predicting 5-min series of carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations in proximity to an intersection. The proposed model is examined based on the measured data under two situations. As the measured pollutant concentrations are found to be dependent on the distance to the intersection, the model is evaluated in three locations respectively, i.e. 110 m, 330 m and 500 m. Due to the different variation of pollutant concentrations on varied time, the model is also evaluated in peak and off-peak traffic time periods separately. Additionally, the proposed model, together with the back-propagation neural network (BPNN), is examined with the measured data in these situations. The proposed model is found to perform better in predictability and precision for both CO and PM2.5 than BPNN does, implying that the hybrid model can be an effective tool to improve the accuracy of estimating pollutants' distribution pattern at intersections. The outputs of these findings demonstrate the potential of the proposed model to be applicable to forecast the distribution pattern of air pollution in real-time in proximity to road intersection.

Hybrid Multi-Objective Optimization of Folsom Reservoir Operation to Maximize Storage in Whole Watershed

NASA Astrophysics Data System (ADS)

Goharian, E.; Gailey, R.; Maples, S.; Azizipour, M.; Sandoval Solis, S.; Fogg, G. E.

2017-12-01

The drought incidents and growing water scarcity in California have a profound effect on human, agricultural, and environmental water needs. California experienced multi-year droughts, which have caused groundwater overdraft and dropping groundwater levels, and dwindling of major reservoirs. These concerns call for a stringent evaluation of future water resources sustainability and security in the state. To answer to this call, Sustainable Groundwater Management Act (SGMA) was passed in 2014 to promise a sustainable groundwater management in California by 2042. SGMA refers to managed aquifer recharge (MAR) as a key management option, especially in areas with high variation in water availability intra- and inter-annually, to secure the refill of underground water storage and return of groundwater quality to a desirable condition. The hybrid optimization of an integrated water resources system provides an opportunity to adapt surface reservoir operations for enhancement in groundwater recharge. Here, to re-operate Folsom Reservoir, objectives are maximizing the storage in the whole American-Cosumnes watershed and maximizing hydropower generation from Folsom Reservoir. While a linear programing (LP) module tends to maximize the total groundwater recharge by distributing and spreading water over suitable lands in basin, a genetic based algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), layer above it controls releases from the reservoir to secure the hydropower generation, carry-over storage in reservoir, available water for replenishment, and downstream water requirements. The preliminary results show additional releases from the reservoir for groundwater recharge during high flow seasons. Moreover, tradeoffs between the objectives describe that new operation performs satisfactorily to increase the storage in the basin, with nonsignificant effects on other objectives.

A Hybrid Algorithm for Non-negative Matrix Factorization Based on Symmetric Information Divergence

PubMed Central

Devarajan, Karthik; Ebrahimi, Nader; Soofi, Ehsan

2017-01-01

The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems. PMID:28868206

Species collapse via hybridization in Darwin's tree finches.

PubMed

Kleindorfer, Sonia; O'Connor, Jody A; Dudaniec, Rachael Y; Myers, Steven A; Robertson, Jeremy; Sulloway, Frank J

2014-03-01

Species hybridization can lead to fitness costs, species collapse, and novel evolutionary trajectories in changing environments. Hybridization is predicted to be more common when environmental conditions change rapidly. Here, we test patterns of hybridization in three sympatric tree finch species (small tree finch Camarhynchus parvulus, medium tree finch Camarhynchus pauper, and large tree finch: Camarhynchus psittacula) that are currently recognized on Floreana Island, Galápagos Archipelago. Genetic analysis of microsatellite data from contemporary samples showed two genetic populations and one hybrid cluster in both 2005 and 2010; hybrid individuals were derived from genetic population 1 (small morph) and genetic population 2 (large morph). Females of the large and rare species were more likely to pair with males of the small common species. Finch populations differed in morphology in 1852-1906 compared with 2005/2010. An unsupervised clustering method showed (a) support for three morphological clusters in the historical tree finch sample (1852-1906), which is consistent with current species recognition; (b) support for two or three morphological clusters in 2005 with some (19%) hybridization; and (c) support for just two morphological clusters in 2010 with frequent (41%) hybridization. We discuss these findings in relation to species demarcations of Camarhynchus tree finches on Floreana Island.

Differential evolution-simulated annealing for multiple sequence alignment

NASA Astrophysics Data System (ADS)

Addawe, R. C.; Addawe, J. M.; Sueño, M. R. K.; Magadia, J. C.

2017-10-01

Multiple sequence alignments (MSA) are used in the analysis of molecular evolution and sequence structure relationships. In this paper, a hybrid algorithm, Differential Evolution - Simulated Annealing (DESA) is applied in optimizing multiple sequence alignments (MSAs) based on structural information, non-gaps percentage and totally conserved columns. DESA is a robust algorithm characterized by self-organization, mutation, crossover, and SA-like selection scheme of the strategy parameters. Here, the MSA problem is treated as a multi-objective optimization problem of the hybrid evolutionary algorithm, DESA. Thus, we name the algorithm as DESA-MSA. Simulated sequences and alignments were generated to evaluate the accuracy and efficiency of DESA-MSA using different indel sizes, sequence lengths, deletion rates and insertion rates. The proposed hybrid algorithm obtained acceptable solutions particularly for the MSA problem evaluated based on the three objectives.

An efficient hybrid method for stochastic reaction-diffusion biochemical systems with delay

NASA Astrophysics Data System (ADS)

Sayyidmousavi, Alireza; Ilie, Silvana

2017-12-01

Many chemical reactions, such as gene transcription and translation in living cells, need a certain time to finish once they are initiated. Simulating stochastic models of reaction-diffusion systems with delay can be computationally expensive. In the present paper, a novel hybrid algorithm is proposed to accelerate the stochastic simulation of delayed reaction-diffusion systems. The delayed reactions may be of consuming or non-consuming delay type. The algorithm is designed for moderately stiff systems in which the events can be partitioned into slow and fast subsets according to their propensities. The proposed algorithm is applied to three benchmark problems and the results are compared with those of the delayed Inhomogeneous Stochastic Simulation Algorithm. The numerical results show that the new hybrid algorithm achieves considerable speed-up in the run time and very good accuracy.

A Network Selection Algorithm Considering Power Consumption in Hybrid Wireless Networks

NASA Astrophysics Data System (ADS)

Joe, Inwhee; Kim, Won-Tae; Hong, Seokjoon

In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.

A new effective operator for the hybrid algorithm for solving global optimisation problems

NASA Astrophysics Data System (ADS)

Duc, Le Anh; Li, Kenli; Nguyen, Tien Trong; Yen, Vu Minh; Truong, Tung Khac

2018-04-01

Hybrid algorithms have been recently used to solve complex single-objective optimisation problems. The ultimate goal is to find an optimised global solution by using these algorithms. Based on the existing algorithms (HP_CRO, PSO, RCCRO), this study proposes a new hybrid algorithm called MPC (Mean-PSO-CRO), which utilises a new Mean-Search Operator. By employing this new operator, the proposed algorithm improves the search ability on areas of the solution space that the other operators of previous algorithms do not explore. Specifically, the Mean-Search Operator helps find the better solutions in comparison with other algorithms. Moreover, the authors have proposed two parameters for balancing local and global search and between various types of local search, as well. In addition, three versions of this operator, which use different constraints, are introduced. The experimental results on 23 benchmark functions, which are used in previous works, show that our framework can find better optimal or close-to-optimal solutions with faster convergence speed for most of the benchmark functions, especially the high-dimensional functions. Thus, the proposed algorithm is more effective in solving single-objective optimisation problems than the other existing algorithms.

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Park, Won-Woong; Kim, Dong-Kyu; Im, Yong-Taek; Bureerat, Sujin; Kwon, Hyuck-Cheol; Chun, Myung-Sik

2015-04-01

This article proposes an efficient metaheuristic based on hybridization of teaching-learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching-learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.

Hybrid dose calculation: a dose calculation algorithm for microbeam radiation therapy

NASA Astrophysics Data System (ADS)

Donzelli, Mattia; Bräuer-Krisch, Elke; Oelfke, Uwe; Wilkens, Jan J.; Bartzsch, Stefan

2018-02-01

Microbeam radiation therapy (MRT) is still a preclinical approach in radiation oncology that uses planar micrometre wide beamlets with extremely high peak doses, separated by a few hundred micrometre wide low dose regions. Abundant preclinical evidence demonstrates that MRT spares normal tissue more effectively than conventional radiation therapy, at equivalent tumour control. In order to launch first clinical trials, accurate and efficient dose calculation methods are an inevitable prerequisite. In this work a hybrid dose calculation approach is presented that is based on a combination of Monte Carlo and kernel based dose calculation. In various examples the performance of the algorithm is compared to purely Monte Carlo and purely kernel based dose calculations. The accuracy of the developed algorithm is comparable to conventional pure Monte Carlo calculations. In particular for inhomogeneous materials the hybrid dose calculation algorithm out-performs purely convolution based dose calculation approaches. It is demonstrated that the hybrid algorithm can efficiently calculate even complicated pencil beam and cross firing beam geometries. The required calculation times are substantially lower than for pure Monte Carlo calculations.

Dynamic modeling and adaptive vibration suppression of a high-speed macro-micro manipulator

NASA Astrophysics Data System (ADS)

Yang, Yi-ling; Wei, Yan-ding; Lou, Jun-qiang; Fu, Lei; Fang, Sheng; Chen, Te-huan

2018-05-01

This paper presents a dynamic modeling and microscopic vibration suppression for a flexible macro-micro manipulator dedicated to high-speed operation. The manipulator system mainly consists of a macro motion stage and a flexible micromanipulator bonded with one macro-fiber-composite actuator. Based on Hamilton's principle and the Bouc-Wen hysteresis equation, the nonlinear dynamic model is obtained. Then, a hybrid control scheme is proposed to simultaneously suppress the elastic vibration during and after the motor motion. In particular, the hybrid control strategy is composed of a trajectory planning approach and an adaptive variable structure control. Moreover, two optimization indices regarding the comprehensive torques and synthesized vibrations are designed, and the optimal trajectories are acquired using a genetic algorithm. Furthermore, a nonlinear fuzzy regulator is used to adjust the switching gain in the variable structure control. Thus, a fuzzy variable structure control with nonlinear adaptive control law is achieved. A series of experiments are performed to verify the effectiveness and feasibility of the established system model and hybrid control strategy. The excited vibration during the motor motion and the residual vibration after the motor motion are decreased. Meanwhile, the settling time is shortened. Both the manipulation stability and operation efficiency of the manipulator are improved by the proposed hybrid strategy.

Hybrid intelligent monironing systems for thermal power plant trips

NASA Astrophysics Data System (ADS)

Barsoum, Nader; Ismail, Firas Basim

2012-11-01

Steam boiler is one of the main equipment in thermal power plants. If the steam boiler trips it may lead to entire shutdown of the plant, which is economically burdensome. Early boiler trips monitoring is crucial to maintain normal and safe operational conditions. In the present work two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MATLAB environment. The training and validation of the two systems has been performed using real operational data captured from the plant control system of selected power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables has been proposed for IMSs data analysis. The first IMS represents the use of pure Artificial Neural Network system for boiler trip detection. All seven boiler trips under consideration have been detected by IMSs before or at the same time of the plant control system. The second IMS represents the use of Genetic Algorithms and Artificial Neural Networks as a hybrid intelligent system. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables performed successfully by the hybrid intelligent system.

4E analysis and multi objective optimization of a micro gas turbine and solid oxide fuel cell hybrid combined heat and power system

NASA Astrophysics Data System (ADS)

Sanaye, Sepehr; Katebi, Arash

2014-02-01

Energy, exergy, economic and environmental (4E) analysis and optimization of a hybrid solid oxide fuel cell and micro gas turbine (SOFC-MGT) system for use as combined generation of heat and power (CHP) is investigated in this paper. The hybrid system is modeled and performance related results are validated using available data in literature. Then a multi-objective optimization approach based on genetic algorithm is incorporated. Eight system design parameters are selected for the optimization procedure. System exergy efficiency and total cost rate (including capital or investment cost, operational cost and penalty cost of environmental emissions) are the two objectives. The effects of fuel unit cost, capital investment and system power output on optimum design parameters are also investigated. It is observed that the most sensitive and important design parameter in the hybrid system is fuel cell current density which has a significant effect on the balance between system cost and efficiency. The selected design point from the Pareto distribution of optimization results indicates a total system exergy efficiency of 60.7%, with estimated electrical energy cost 0.057 kW-1 h-1, and payback period of about 6.3 years for the investment.

Hybrid antenna arrays with non-uniform Electromagnetic Band Gap lattices for wireless communication networks

NASA Astrophysics Data System (ADS)

Mourtzios, Ch.; Siakavara, K.

2015-08-01

A method to design hybrid antenna configurations with very low profile, suitable for smart and Multiple Input-Multiple Output antenna systems is proposed. The antennas are incorporated with novel Electromagnetic Band Gap (EBG) surfaces with non-similar cells. These non-uniform EBG surfaces have been properly designed to cause focusing, of the incident waves, thus enhancing the characteristics of operation of antenna elements positioned in close proximity to the surface and also to increase the isolation between them. Theoretical analysis of the reflection mechanism of this type of lattices as well as the prediction of the resulting performance of the antenna is presented. All these considerations are validated with implementation and simulation of the hybrid structures inside the Universal Mobile Telecommunications System frequency band. The results show that increment of the gain and isolation between the antenna elements can be obtained. Moreover, results for the correlation coefficient between the elements, for Gaussian distribution of the incoming waves have been received and the tolerance of the antennas to the variation of the polarization characteristics of the incoming waves has been investigated. A Genetic Algorithm has been constructed and applied to find the proper geometry of the hybrid antennas in order the correlation coefficient to be minimized and get almost independent from the polarization of incident waves.

Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach.

PubMed

Ge, Yujia; Xu, Bin

2016-01-01

Resource allocation could be influenced by various dynamic elements, such as the skills of engineers and the growth of skills, which requires managers to find an effective and efficient tool to support their staffing decision-making processes. Rescheduling happens commonly and frequently during the project execution. Control options have to be made when new resources are added or tasks are changed. In this paper we propose a software project staffing model considering dynamic elements of staff productivity with a Genetic Algorithm (GA) and Hill Climbing (HC) based optimizer. Since a newly generated reschedule dramatically different from the initial schedule could cause an obvious shifting cost increase, our rescheduling strategies consider both efficiency and stability. The results of real world case studies and extensive simulation experiments show that our proposed method is effective and could achieve comparable performance to other heuristic algorithms in most cases.

Data mining in soft computing framework: a survey.

PubMed

Mitra, S; Pal, S K; Mitra, P

2002-01-01

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach

PubMed Central

Ge, Yujia; Xu, Bin

2016-01-01

Resource allocation could be influenced by various dynamic elements, such as the skills of engineers and the growth of skills, which requires managers to find an effective and efficient tool to support their staffing decision-making processes. Rescheduling happens commonly and frequently during the project execution. Control options have to be made when new resources are added or tasks are changed. In this paper we propose a software project staffing model considering dynamic elements of staff productivity with a Genetic Algorithm (GA) and Hill Climbing (HC) based optimizer. Since a newly generated reschedule dramatically different from the initial schedule could cause an obvious shifting cost increase, our rescheduling strategies consider both efficiency and stability. The results of real world case studies and extensive simulation experiments show that our proposed method is effective and could achieve comparable performance to other heuristic algorithms in most cases. PMID:27285420

New evidence for hybrid zones of forest and savanna elephants in Central and West Africa.

PubMed

Mondol, Samrat; Moltke, Ida; Hart, John; Keigwin, Michael; Brown, Lisa; Stephens, Matthew; Wasser, Samuel K

2015-12-01

The African elephant consists of forest and savanna subspecies. Both subspecies are highly endangered due to severe poaching and habitat loss, and knowledge of their population structure is vital to their conservation. Previous studies have demonstrated marked genetic and morphological differences between forest and savanna elephants, and despite extensive sampling, genetic evidence of hybridization between them has been restricted largely to a few hybrids in the Garamba region of northeastern Democratic Republic of Congo (DRC). Here, we present new genetic data on hybridization from previously unsampled areas of Africa. Novel statistical methods applied to these data identify 46 hybrid samples--many more than have been previously identified--only two of which are from the Garamba region. The remaining 44 are from three other geographically distinct locations: a major hybrid zone along the border of the DRC and Uganda, a second potential hybrid zone in Central African Republic and a smaller fraction of hybrids in the Pendjari-Arli complex of West Africa. Most of the hybrids show evidence of interbreeding over more than one generation, demonstrating that hybrids are fertile. Mitochondrial and Y chromosome data demonstrate that the hybridization is bidirectional, involving males and females from both subspecies. We hypothesize that the hybrid zones may have been facilitated by poaching and habitat modification. The localized geography and rarity of hybrid zones, their possible facilitation from human pressures, and the high divergence and genetic distinctness of forest and savanna elephants throughout their ranges, are consistent with calls for separate species classification. © 2015 John Wiley & Sons Ltd.

Tissue culture-induced genetic and epigenetic alterations in rice pure-lines, F1 hybrids and polyploids

PubMed Central

2013-01-01

Background Genetic and epigenetic alterations can be invoked by plant tissue culture, which may result in heritable changes in phenotypes, a phenomenon collectively termed somaclonal variation. Although extensive studies have been conducted on the molecular nature and spectrum of tissue culture-induced genomic alterations, the issue of whether and to what extent distinct plant genotypes, e.g., pure-lines, hybrids and polyploids, may respond differentially to the tissue culture condition remains poorly understood. Results We investigated tissue culture-induced genetic and epigenetic alterations in a set of rice genotypes including two pure-lines (different subspecies), a pair of reciprocal F1 hybrids parented by the two pure-lines, and a pair of reciprocal tetraploids resulted from the hybrids. Using two molecular markers, amplified fragment length polymorphism (AFLP) and methylation-sensitive amplified polymorphism (MSAP), both genetic and DNA methylation alterations were detected in calli and regenerants from all six genotypes, but genetic alteration is more prominent than epigenetic alteration. While significant genotypic difference was observed in frequencies of both types of alterations, only genetic alteration showed distinctive features among the three types of genomes, with one hybrid (N/9) being exceptionally labile. Surprisingly, difference in genetic alteration frequencies between the pair of reciprocal F1 hybrids is much greater than that between the two pure-line subspecies. Difference also exists in the pair of reciprocal tetraploids, but is to a less extent than that between the hybrids. The steady-state transcript abundance of genes involved in DNA repair and DNA methylation was significantly altered in both calli and regenerants, and some of which were correlated with the genetic and/or epigenetic alterations. Conclusions Our results, based on molecular marker analysis of ca. 1,000 genomic loci, document that genetic alteration is the major cause of somaclonal variation in rice, which is concomitant with epigenetic alterations. Perturbed expression by tissue culture of a set of 41 genes encoding for enzymes involved in DNA repair and DNA methylation is associated with both genetic and epigenetic alterations. There exist fundamental differences among distinct genotypes, pure-lines, hybrids and tetraploids, in propensities of generating both genetic and epigenetic alterations under the tissue culture condition. Parent-of-origin has a conspicuous effect on the alteration frequencies. PMID:23642214

Tissue culture-induced genetic and epigenetic alterations in rice pure-lines, F1 hybrids and polyploids.

PubMed

Wang, Xiaoran; Wu, Rui; Lin, Xiuyun; Bai, Yan; Song, Congdi; Yu, Xiaoming; Xu, Chunming; Zhao, Na; Dong, Yuzhu; Liu, Bao

2013-05-05

Genetic and epigenetic alterations can be invoked by plant tissue culture, which may result in heritable changes in phenotypes, a phenomenon collectively termed somaclonal variation. Although extensive studies have been conducted on the molecular nature and spectrum of tissue culture-induced genomic alterations, the issue of whether and to what extent distinct plant genotypes, e.g., pure-lines, hybrids and polyploids, may respond differentially to the tissue culture condition remains poorly understood. We investigated tissue culture-induced genetic and epigenetic alterations in a set of rice genotypes including two pure-lines (different subspecies), a pair of reciprocal F1 hybrids parented by the two pure-lines, and a pair of reciprocal tetraploids resulted from the hybrids. Using two molecular markers, amplified fragment length polymorphism (AFLP) and methylation-sensitive amplified polymorphism (MSAP), both genetic and DNA methylation alterations were detected in calli and regenerants from all six genotypes, but genetic alteration is more prominent than epigenetic alteration. While significant genotypic difference was observed in frequencies of both types of alterations, only genetic alteration showed distinctive features among the three types of genomes, with one hybrid (N/9) being exceptionally labile. Surprisingly, difference in genetic alteration frequencies between the pair of reciprocal F1 hybrids is much greater than that between the two pure-line subspecies. Difference also exists in the pair of reciprocal tetraploids, but is to a less extent than that between the hybrids. The steady-state transcript abundance of genes involved in DNA repair and DNA methylation was significantly altered in both calli and regenerants, and some of which were correlated with the genetic and/or epigenetic alterations. Our results, based on molecular marker analysis of ca. 1,000 genomic loci, document that genetic alteration is the major cause of somaclonal variation in rice, which is concomitant with epigenetic alterations. Perturbed expression by tissue culture of a set of 41 genes encoding for enzymes involved in DNA repair and DNA methylation is associated with both genetic and epigenetic alterations. There exist fundamental differences among distinct genotypes, pure-lines, hybrids and tetraploids, in propensities of generating both genetic and epigenetic alterations under the tissue culture condition. Parent-of-origin has a conspicuous effect on the alteration frequencies.

Routing Algorithm based on Minimum Spanning Tree and Minimum Cost Flow for Hybrid Wireless-optical Broadband Access Network

NASA Astrophysics Data System (ADS)

Le, Zichun; Suo, Kaihua; Fu, Minglei; Jiang, Ling; Dong, Wen

2012-03-01

In order to minimize the average end to end delay for data transporting in hybrid wireless optical broadband access network, a novel routing algorithm named MSTMCF (minimum spanning tree and minimum cost flow) is devised. The routing problem is described as a minimum spanning tree and minimum cost flow model and corresponding algorithm procedures are given. To verify the effectiveness of MSTMCF algorithm, extensively simulations based on OWNS have been done under different types of traffic source.

Genetic variability in Jatropha curcas L. from diallel crossing.

PubMed

Ribeiro, D O; Silva-Mann, R; Alvares-Carvalho, S V; Souza, E M S; Vasconcelos, M C; Blank, A F

2017-05-18

Physic nut (Jatropha curcas L.) presents high oilseed yield and low production cost. However, technical-scientific knowledge on this crop is still limited. This study aimed to evaluate and estimate the genetic variability of hybrids obtained from dialell crossing. Genetic variability was carried out using ISSR molecular markers. For genetic variability, nine primers were used, and six were selected with 80.7% polymorphism. Genetic similarity was obtained using the NTSYS pc. 2.1 software, and cluster analysis was obtained by the UPGMA method. Mean genetic similarity was 58.4% among hybrids; the most divergent pair was H1 and H10 and the most similar pair was H9 and H10. ISSR PCR markers provided a quick and highly informative system for DNA fingerprinting, and also allowed establishing genetic relationships of Jatropha hybrids.

Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply chain environment.

PubMed

Wang, Lin; Qu, Hui; Liu, Shan; Dun, Cai-xia

2013-01-01

As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted.

Modeling and Optimization of the Multiobjective Stochastic Joint Replenishment and Delivery Problem under Supply Chain Environment

PubMed Central

Dun, Cai-xia

2013-01-01

As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted. PMID:24302880

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

Genetic Evidence of Hybridization between the Endangered Native Species Iguana delicatissima and the Invasive Iguana iguana (Reptilia, Iguanidae) in the Lesser Antilles: Management Implications.

PubMed

Vuillaume, Barbara; Valette, Victorien; Lepais, Olivier; Grandjean, Frédéric; Breuil, Michel

2015-01-01

The worldwide increase of hybridization in different groups is thought to have become more important with the loss of isolating barriers and the introduction of invasive species. This phenomenon could result in the extinction of endemic species. This study aims at investigating the hybridization dynamics between the endemic and threatened Lesser Antillean iguana (Iguana delicatissima) and the invasive common green iguana (Iguana iguana) in the Lesser Antilles, as well as assessing the impact of interspecific hybridization on the decline of I. delicatissima. 59 I. delicatissima (5 localities), 47 I. iguana (12 localities) and 27 hybrids (5 localities), who were all identified based on morphological characters, have been genotyped at 15 microsatellites markers. We also sequenced hybrids using ND4 mitochondrial loci to further investigate mitochondrial introgression. The genetic clustering of species and hybrid genetic assignment were performed using a comparative approach, through the implementation of a Discriminant Analysis of Principal Component (DAPC) based on statistics, as well as genetic clustering approaches based on the genetic models of several populations (Structure, NewHybrids and HIest), in order to get full characterization of hybridization patterns and introgression dynamics across the islands. The iguanas identified as hybrids in the wild, thanks to morphological analysis, were all genetically F1, F2, or backcrosses. A high proportion of individuals were also the result of a longer-term admixture. The absence of reproductive barriers between species leads to hybridization when species are in contact. Yet morphological and behavioral differences between species could explain why males I. iguana may dominate I. delicatissima, thus resulting in short-term species displacement and extinction by hybridization and recurrent introgression from I. iguana toward I. delicatissima. As a consequence, I. delicatissima gets eliminated through introgression, as observed in recent population history over several islands. These results have profound implications for species management of the endangered I. delicatissima and practical conservation recommendations are being discussed in the light of these findings.

Genetic Evidence of Hybridization between the Endangered Native Species Iguana delicatissima and the Invasive Iguana iguana (Reptilia, Iguanidae) in the Lesser Antilles: Management Implications

PubMed Central

Vuillaume, Barbara; Valette, Victorien; Lepais, Olivier; Grandjean, Frédéric; Breuil, Michel

2015-01-01

The worldwide increase of hybridization in different groups is thought to have become more important with the loss of isolating barriers and the introduction of invasive species. This phenomenon could result in the extinction of endemic species. This study aims at investigating the hybridization dynamics between the endemic and threatened Lesser Antillean iguana (Iguana delicatissima) and the invasive common green iguana (Iguana iguana) in the Lesser Antilles, as well as assessing the impact of interspecific hybridization on the decline of I. delicatissima. 59 I. delicatissima (5 localities), 47 I. iguana (12 localities) and 27 hybrids (5 localities), who were all identified based on morphological characters, have been genotyped at 15 microsatellites markers. We also sequenced hybrids using ND4 mitochondrial loci to further investigate mitochondrial introgression. The genetic clustering of species and hybrid genetic assignment were performed using a comparative approach, through the implementation of a Discriminant Analysis of Principal Component (DAPC) based on statistics, as well as genetic clustering approaches based on the genetic models of several populations (Structure, NewHybrids and HIest), in order to get full characterization of hybridization patterns and introgression dynamics across the islands. The iguanas identified as hybrids in the wild, thanks to morphological analysis, were all genetically F1, F2, or backcrosses. A high proportion of individuals were also the result of a longer-term admixture. The absence of reproductive barriers between species leads to hybridization when species are in contact. Yet morphological and behavioral differences between species could explain why males I. iguana may dominate I. delicatissima, thus resulting in short-term species displacement and extinction by hybridization and recurrent introgression from I. iguana toward I. delicatissima. As a consequence, I. delicatissima gets eliminated through introgression, as observed in recent population history over several islands. These results have profound implications for species management of the endangered I. delicatissima and practical conservation recommendations are being discussed in the light of these findings. PMID:26046351

Genetic analysis of an ephemeral intraspecific hybrid zone in the hypervariable tree, Metrosideros polymorpha, on Hawai'i Island.

PubMed

Stacy, E A; Johansen, J B; Sakishima, T; Price, D K

2016-09-01

Intraspecific hybrid zones involving long-lived woody species are rare and can provide insights into the genetic basis of early-diverging traits in speciation. Within the landscape-dominant Hawaiian tree, Metrosideros polymorpha, are morphologically distinct successional varieties, incana and glaberrima, that dominate new and old lava flows, respectively, below 1200 me on volcanically active Hawai'i Island, with var. glaberrima also extending to higher elevations and bogs. Here, we use morphological measurements on 86 adult trees to document the presence of an incana-glaberrima hybrid zone on the 1855 Mauna Loa lava flow on east Hawai'i Island and parent-offspring analysis of 1311 greenhouse seedlings from 71 crosses involving 72 adults to estimate heritabilities and genetic correlations among vegetative traits. Both the variation in adult leaf pubescence at the site and the consistency between adult and offspring phenotypes suggest the presence of two hybrid classes, F1s and var. incana backcrosses, as would be expected on a relatively young lava flow. Nine nuclear microsatellite loci failed to distinguish parental and hybrid genotypes. All four leaf traits examined showed an additive genetic basis with moderate to strong heritabilities, and genetic correlations were stronger for the more range-restricted var. incana. The differences between varieties in trait values, heritabilities and genetic correlations, coupled with high genetic variation within but low genetic variation between varieties, are consistent with a multi-million-year history of alternating periods of disruptive selection in contrasting environments and admixture in ephemeral hybrid zones. Finally, the contrasting genetic architectures suggest different evolutionary trajectories of leaf traits in these forms.

Genetic analysis of an ephemeral intraspecific hybrid zone in the hypervariable tree, Metrosideros polymorpha, on Hawai‘i Island

PubMed Central

Stacy, E A; Johansen, J B; Sakishima, T; Price, D K

2016-01-01

Intraspecific hybrid zones involving long-lived woody species are rare and can provide insights into the genetic basis of early-diverging traits in speciation. Within the landscape-dominant Hawaiian tree, Metrosideros polymorpha, are morphologically distinct successional varieties, incana and glaberrima, that dominate new and old lava flows, respectively, below 1200 me on volcanically active Hawai‘i Island, with var. glaberrima also extending to higher elevations and bogs. Here, we use morphological measurements on 86 adult trees to document the presence of an incana–glaberrima hybrid zone on the 1855 Mauna Loa lava flow on east Hawai‘i Island and parent–offspring analysis of 1311 greenhouse seedlings from 71 crosses involving 72 adults to estimate heritabilities and genetic correlations among vegetative traits. Both the variation in adult leaf pubescence at the site and the consistency between adult and offspring phenotypes suggest the presence of two hybrid classes, F1s and var. incana backcrosses, as would be expected on a relatively young lava flow. Nine nuclear microsatellite loci failed to distinguish parental and hybrid genotypes. All four leaf traits examined showed an additive genetic basis with moderate to strong heritabilities, and genetic correlations were stronger for the more range-restricted var. incana. The differences between varieties in trait values, heritabilities and genetic correlations, coupled with high genetic variation within but low genetic variation between varieties, are consistent with a multi-million-year history of alternating periods of disruptive selection in contrasting environments and admixture in ephemeral hybrid zones. Finally, the contrasting genetic architectures suggest different evolutionary trajectories of leaf traits in these forms. PMID:27301333

Optimization of heterogeneous Bin packing using adaptive genetic algorithm

NASA Astrophysics Data System (ADS)

Sridhar, R.; Chandrasekaran, M.; Sriramya, C.; Page, Tom

2017-03-01

This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.

Analysis of genetic and environmental effects on hybrid poplar rooting in Central and Northern Minnesota, USA

Treesearch

Ronald S., Jr. Zalesny; Don Riemenschneider; Edmund Bauer

2000-01-01

We studied genetic and environmental effects on adventitious root initiation and growth because rooting is biologically prerequisite to the establishment of hybrid poplar plantations. Six clones from two pedigrees (pure Populus deltoides "cottonwoods" and P. deltoides x P. maximowiczii hybrids) were...

Hybrid sparse blind deconvolution: an implementation of SOOT algorithm to real data

NASA Astrophysics Data System (ADS)

Pakmanesh, Parvaneh; Goudarzi, Alireza; Kourki, Meisam

2018-06-01

Getting information of seismic data depends on deconvolution as an important processing step; it provides the reflectivity series by signal compression. This compression can be obtained by removing the wavelet effects on the traces. The recently blind deconvolution has provided reliable performance for sparse signal recovery. In this study, two deconvolution methods have been implemented to the seismic data; the convolution of these methods provides a robust spiking deconvolution approach. This hybrid deconvolution is applied using the sparse deconvolution (MM algorithm) and the Smoothed-One-Over-Two algorithm (SOOT) in a chain. The MM algorithm is based on the minimization of the cost function defined by standards l1 and l2. After applying the two algorithms to the seismic data, the SOOT algorithm provided well-compressed data with a higher resolution than the MM algorithm. The SOOT algorithm requires initial values to be applied for real data, such as the wavelet coefficients and reflectivity series that can be achieved through the MM algorithm. The computational cost of the hybrid method is high, and it is necessary to be implemented on post-stack or pre-stack seismic data of complex structure regions.

Effects of Hybridization and Evolutionary Constraints on Secondary Metabolites: The Genetic Architecture of Phenylpropanoids in European Populus Species

PubMed Central

Caseys, Celine; Stritt, Christoph; Glauser, Gaetan; Blanchard, Thierry; Lexer, Christian

2015-01-01

The mechanisms responsible for the origin, maintenance and evolution of plant secondary metabolite diversity remain largely unknown. Decades of phenotypic studies suggest hybridization as a key player in generating chemical diversity in plants. Knowledge of the genetic architecture and selective constraints of phytochemical traits is key to understanding the effects of hybridization on plant chemical diversity and ecological interactions. Using the European Populus species P. alba (White poplar) and P. tremula (European aspen) and their hybrids as a model, we examined levels of inter- and intraspecific variation, heritabilities, phenotypic correlations, and the genetic architecture of 38 compounds of the phenylpropanoid pathway measured by liquid chromatography and mass spectrometry (UHPLC-MS). We detected 41 quantitative trait loci (QTL) for chlorogenic acids, salicinoids and flavonoids by genetic mapping in natural hybrid crosses. We show that these three branches of the phenylpropanoid pathway exhibit different geographic patterns of variation, heritabilities, and genetic architectures, and that they are affected differently by hybridization and evolutionary constraints. Flavonoid abundances present high species specificity, clear geographic structure, and strong genetic determination, contrary to salicinoids and chlorogenic acids. Salicinoids, which represent important defence compounds in Salicaceae, exhibited pronounced genetic correlations on the QTL map. Our results suggest that interspecific phytochemical differentiation is concentrated in downstream sections of the phenylpropanoid pathway. In particular, our data point to glycosyltransferase enzymes as likely targets of rapid evolution and interspecific differentiation in the ‘model forest tree’ Populus. PMID:26010156

Effects of hybridization and evolutionary constraints on secondary metabolites: the genetic architecture of phenylpropanoids in European populus species.

PubMed

Caseys, Celine; Stritt, Christoph; Glauser, Gaetan; Blanchard, Thierry; Lexer, Christian

2015-01-01

The mechanisms responsible for the origin, maintenance and evolution of plant secondary metabolite diversity remain largely unknown. Decades of phenotypic studies suggest hybridization as a key player in generating chemical diversity in plants. Knowledge of the genetic architecture and selective constraints of phytochemical traits is key to understanding the effects of hybridization on plant chemical diversity and ecological interactions. Using the European Populus species P. alba (White poplar) and P. tremula (European aspen) and their hybrids as a model, we examined levels of inter- and intraspecific variation, heritabilities, phenotypic correlations, and the genetic architecture of 38 compounds of the phenylpropanoid pathway measured by liquid chromatography and mass spectrometry (UHPLC-MS). We detected 41 quantitative trait loci (QTL) for chlorogenic acids, salicinoids and flavonoids by genetic mapping in natural hybrid crosses. We show that these three branches of the phenylpropanoid pathway exhibit different geographic patterns of variation, heritabilities, and genetic architectures, and that they are affected differently by hybridization and evolutionary constraints. Flavonoid abundances present high species specificity, clear geographic structure, and strong genetic determination, contrary to salicinoids and chlorogenic acids. Salicinoids, which represent important defence compounds in Salicaceae, exhibited pronounced genetic correlations on the QTL map. Our results suggest that interspecific phytochemical differentiation is concentrated in downstream sections of the phenylpropanoid pathway. In particular, our data point to glycosyltransferase enzymes as likely targets of rapid evolution and interspecific differentiation in the 'model forest tree' Populus.

Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

NASA Astrophysics Data System (ADS)

Tang, Qiuhua; Li, Zixiang; Zhang, Liping; Floudas, C. A.; Cao, Xiaojun

2015-09-01

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Genetic basis of aboveground productivity in two native Populus species and their hybrids.

PubMed

Lojewski, Nathan R; Fischer, Dylan G; Bailey, Joseph K; Schweitzer, Jennifer A; Whitham, Thomas G; Hart, Stephen C

2009-09-01

Demonstration of genetic control over riparian tree productivity has major implications for responses of riparian systems to shifting environmental conditions and effects of genetics on ecosystems in general. We used field studies and common gardens, applying both molecular and quantitative techniques, to compare plot-level tree aboveground net primary productivity (ANPP(tree)) and individual tree growth rate constants in relation to plant genetic identity in two naturally occurring Populus tree species and their hybrids. In field comparisons of four cross types (Populus fremontii S. Wats., Populus angustifolia James, F(1) hybrids and backcross hybrids) across 11 natural stands, productivity was greatest for P. fremontii trees, followed by hybrids and lowest in P. angustifolia. A similar pattern was observed in four common gardens across a 290 m elevation and 100 km environmental gradient. Despite a doubling in productivity across the common gardens, the relative differences among the cross types remained constant. Using clonal replicates in a common garden, we found ANPP(tree) to be a heritable plant trait (i.e., broad-sense heritability), such that plant genetic factors explained between 38% and 82% of the variation in ANPP(tree). Furthermore, analysis of the genetic composition among individual tree genotypes using restriction fragment length polymorphism molecular markers showed that genetically similar trees also exhibited similar ANPP(tree). These findings indicate strong genetic contributions to natural variation in ANPP with important ecological implications.

Improved hybridization of Fuzzy Analytic Hierarchy Process (FAHP) algorithm with Fuzzy Multiple Attribute Decision Making - Simple Additive Weighting (FMADM-SAW)

NASA Astrophysics Data System (ADS)

Zaiwani, B. E.; Zarlis, M.; Efendi, S.

2018-03-01

In this research, the improvement of hybridization algorithm of Fuzzy Analytic Hierarchy Process (FAHP) with Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) in selecting the best bank chief inspector based on several qualitative and quantitative criteria with various priorities. To improve the performance of the above research, FAHP algorithm hybridization with Fuzzy Multiple Attribute Decision Making - Simple Additive Weighting (FMADM-SAW) algorithm was adopted, which applied FAHP algorithm to the weighting process and SAW for the ranking process to determine the promotion of employee at a government institution. The result of improvement of the average value of Efficiency Rate (ER) is 85.24%, which means that this research has succeeded in improving the previous research that is equal to 77.82%. Keywords: Ranking and Selection, Fuzzy AHP, Fuzzy TOPSIS, FMADM-SAW.

Information filtering via a scaling-based function.

PubMed

Qiu, Tian; Zhang, Zi-Ke; Chen, Guang

2013-01-01

Finding a universal description of the algorithm optimization is one of the key challenges in personalized recommendation. In this article, for the first time, we introduce a scaling-based algorithm (SCL) independent of recommendation list length based on a hybrid algorithm of heat conduction and mass diffusion, by finding out the scaling function for the tunable parameter and object average degree. The optimal value of the tunable parameter can be abstracted from the scaling function, which is heterogeneous for the individual object. Experimental results obtained from three real datasets, Netflix, MovieLens and RYM, show that the SCL is highly accurate in recommendation. More importantly, compared with a number of excellent algorithms, including the mass diffusion method, the original hybrid method, and even an improved version of the hybrid method, the SCL algorithm remarkably promotes the personalized recommendation in three other aspects: solving the accuracy-diversity dilemma, presenting a high novelty, and solving the key challenge of cold start problem.

Genetic parameters in parents and hybrids of circulant diallel in popcorn.

PubMed

Rangel, R M; Amaral, A T; Scapim, C A; Freitas, S P; Pereira, M G

2008-10-07

With the aim of estimating genetic parameters and identifying superior popcorn combinations, 10 parents were crossed in a circulant diallel and evaluated together with the 15 resulting hybrids at two locations in two growing seasons for grain yield, number of broken plants, number of partially husked ears and popping expansion. The hybrids were less sensitive to environmental variations than the parents of the diallel in the 2003/2004 and 2004/2005 growing seasons. The genetic parameters suggested possible genetic gains for grain yield and popping expansion, mainly. Bidirectional dominance could have occurred for popping expansion. Heterobeltiosis for grain yield seems to be a common effect in popcorn. The intrapopulation breeding for popping expansion may offer superior genetic gains, but for grain yield, interpopulation breeding is required. The performance of UNB2U-C1 x BRS Angela indicated this hybrid for experimental cultivation in the northern and northwestern Fluminense region in Rio de Janeiro State, Brazil.

Abnormalities of hybrid grouper (Epinephelus fuscoguttatus x Epinephelus lanceolatus) in Situbondo

NASA Astrophysics Data System (ADS)

Triastuti, J.; Pursetyo, K. T.; Monica, A.; Lutfiyah, L.; Budi, D. S.

2018-04-01

Grouper is one of consumption fish which is demanded excessively by local consumers and foreign consumers. Hybridization of grouper has been performed considerably that produce the good genetic quality of hybrid variants. One of grouper fish which has good genetic in its growth is kertang grouper fish. Nowadays many hatcheries performing hybridization between kertang grouper fish and tiger grouper fish, however observation of the hybrid abnormality has not been performed yet. Abnormality is able to increase since genetic causes, so that observation of abnormality occurrence in cantang hybrid grouper fish in Situbondo, JawaTimur, Indonesia in May – July in 3 times grading of juvenile stadia was performed. Results showed abnormalities were observed on mouth and operculum, branched of neural arch, fusion of neural arch, fusion of posterior truncus vertebrae, fusion of caudal vertebrae, fusion of anterior truncus vertebrae.

Early events in speciation: Polymorphism for hybrid male sterility in Drosophila

PubMed Central

Reed, Laura K.; Markow, Therese A.

2004-01-01

Capturing the process of speciation early enough to determine the initial genetic causes of reproductive isolation remains a major challenge in evolutionary biology. We have found, to our knowledge, the first example of substantial intraspecific polymorphism for genetic factors contributing to hybrid male sterility. Specifically, we show that the occurrence of hybrid male sterility in crosses between Drosophila mojavensis and its sister species, Drosophila arizonae, is controlled by factors present at different frequencies in different populations of D. mojavensis. In addition, we show that hybrid male sterility is a complex phenotype; some hybrid males with motile sperm still cannot sire offspring. Because male sterility factors in hybrids between these species are not yet fixed within D. mojavensis, this system provides an invaluable opportunity to characterize the genetics of reproductive isolation at an early stage. PMID:15184657

Early events in speciation: polymorphism for hybrid male sterility in Drosophila.

PubMed

Reed, Laura K; Markow, Therese A

2004-06-15

Capturing the process of speciation early enough to determine the initial genetic causes of reproductive isolation remains a major challenge in evolutionary biology. We have found, to our knowledge, the first example of substantial intraspecific polymorphism for genetic factors contributing to hybrid male sterility. Specifically, we show that the occurrence of hybrid male sterility in crosses between Drosophila mojavensis and its sister species, Drosophila arizonae, is controlled by factors present at different frequencies in different populations of D. mojavensis. In addition, we show that hybrid male sterility is a complex phenotype; some hybrid males with motile sperm still cannot sire offspring. Because male sterility factors in hybrids between these species are not yet fixed within D. mojavensis, this system provides an invaluable opportunity to characterize the genetics of reproductive isolation at an early stage.

Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

NASA Astrophysics Data System (ADS)

Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

2009-05-01

Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for multi-season streamflow generation in hydrology are: i) parametric models which hypothesize the form of the periodic dependence structure and the distributional form a priori (examples are PAR, PARMA); disaggregation models that aim to preserve the correlation structure at the periodic level and the aggregated annual level; ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; (k-nearest neighbor (k-NN), matched block bootstrap (MABB)); non-parametric disaggregation model. iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought characteristics has been posing a persistent challenge to the stochastic modeler. This is partly because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares (LS) estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water-use characteristics, which requires large number of trial simulations and inspection of many plots and tables. Still accurate prediction of the storage and the critical drought characteristics may not be ensured. In this study a multi-objective optimization framework is proposed to find the optimal hybrid model (blend of a simple parametric model, PAR(1) model and matched block bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin

2017-09-01

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications.

PubMed

Lei, Guoqing; Dou, Yong; Wan, Wen; Xia, Fei; Li, Rongchun; Ma, Meng; Zou, Dan

2012-01-01

Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.

Three hybridization models based on local search scheme for job shop scheduling problem

NASA Astrophysics Data System (ADS)

Balbi Fraga, Tatiana

2015-05-01

This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.

Genetics of Hybrid Incompatibility Between Lycopersicon esculentum and L. hirsutum

PubMed Central

Moyle, Leonie C.; Graham, Elaine B.

2005-01-01

We examined the genetics of hybrid incompatibility between two closely related diploid hermaphroditic plant species. Using a set of near-isogenic lines (NILs) representing 85% of the genome of the wild species Lycopersicon hirsutum (Solanum habrochaites) in the genetic background of the cultivated tomato L. esculentum (S. lycopersicum), we found that hybrid pollen and seed infertility are each based on 5–11 QTL that individually reduce hybrid fitness by 36–90%. Seed infertility QTL act additively or recessively, consistent with findings in other systems where incompatibility loci have largely been recessive. Genetic lengths of introgressed chromosomal segments explain little of the variation for hybrid incompatibility among NILs, arguing against an infinitesimal model of hybrid incompatibility and reinforcing our inference of a limited number of discrete incompatibility factors between these species. In addition, male (pollen) and other (seed) incompatibility factors are roughly comparable in number. The latter two findings contrast strongly with data from Drosophila where hybrid incompatibility can be highly polygenic and complex, and male sterility evolves substantially faster than female sterility or hybrid inviability. The observed differences between Lycopersicon and Drosophila might be due to differences in sex determination system, reproductive and mating biology, and/or the prevalence of sexual interactions such as sexual selection. PMID:15466436

Evidence of natural selection acting on a polymorphic hybrid incompatibility locus in Mimulus.

PubMed

Sweigart, Andrea L; Flagel, Lex E

2015-02-01

As a common cause of reproductive isolation in diverse taxa, hybrid incompatibilities are fundamentally important to speciation. A key question is which evolutionary forces drive the initial substitutions within species that lead to hybrid dysfunction. Previously, we discovered a simple genetic incompatibility that causes nearly complete male sterility and partial female sterility in hybrids between the two closely related yellow monkeyflower species Mimulus guttatus and M. nasutus. In this report, we fine map the two major incompatibility loci-hybrid male sterility 1 (hms1) and hybrid male sterility 2 (hms2)-to small nuclear genomic regions (each <70 kb) that include strong candidate genes. With this improved genetic resolution, we also investigate the evolutionary dynamics of hms1 in a natural population of M. guttatus known to be polymorphic at this locus. Using classical genetic crosses and population genomics, we show that a 320-kb region containing the hms1 incompatibility allele has risen to intermediate frequency in this population by strong natural selection. This finding provides direct evidence that natural selection within plant species can lead to hybrid dysfunction between species. Copyright © 2015 by the Genetics Society of America.

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

PubMed Central

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. PMID:29768463

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PubMed

Rani R, Hannah Jessie; Victoire T, Aruldoss Albert

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

A formally verified algorithm for interactive consistency under a hybrid fault model

NASA Technical Reports Server (NTRS)

Lincoln, Patrick; Rushby, John

1993-01-01

Consistent distribution of single-source data to replicated computing channels is a fundamental problem in fault-tolerant system design. The 'Oral Messages' (OM) algorithm solves this problem of Interactive Consistency (Byzantine Agreement) assuming that all faults are worst-cass. Thambidurai and Park introduced a 'hybrid' fault model that distinguished three fault modes: asymmetric (Byzantine), symmetric, and benign; they also exhibited, along with an informal 'proof of correctness', a modified version of OM. Unfortunately, their algorithm is flawed. The discipline of mechanically checked formal verification eventually enabled us to develop a correct algorithm for Interactive Consistency under the hybrid fault model. This algorithm withstands $a$ asymmetric, $s$ symmetric, and $b$ benign faults simultaneously, using $m+1$ rounds, provided $n is greater than 2a + 2s + b + m$, and $m\\geg a$. We present this algorithm, discuss its subtle points, and describe its formal specification and verification in PVS. We argue that formal verification systems such as PVS are now sufficiently effective that their application to fault-tolerance algorithms should be considered routine.

Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

NASA Astrophysics Data System (ADS)

Koeppen, W. C.; Pilger, E.; Wright, R.

2011-07-01

We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

Hybrid Speciation in a Marine Mammal: The Clymene Dolphin (Stenella clymene)

PubMed Central

Amaral, Ana R.; Lovewell, Gretchen; Coelho, Maria M.; Amato, George; Rosenbaum, Howard C.

2014-01-01

Natural hybridization may result in the exchange of genetic material between divergent lineages and even the formation of new taxa. Many of the Neo-Darwinian architects argued that, particularly for animal clades, natural hybridization was maladaptive. Recent evidence, however, has falsified this hypothesis, instead indicating that this process may lead to increased biodiversity through the formation of new species. Although such cases of hybrid speciation have been described in plants, fish and insects, they are considered exceptionally rare in mammals. Here we present evidence for a marine mammal, Stenella clymene, arising through natural hybridization. We found phylogenetic discordance between mitochondrial and nuclear markers, which, coupled with a pattern of transgressive segregation seen in the morphometric variation of some characters, support a case of hybrid speciation. S. clymene is currently genetically differentiated from its putative parental species, Stenella coerueloalba and Stenella longisrostris, although low levels of introgressive hybridization may be occurring. Although non-reticulate forms of evolution, such as incomplete lineage sorting, could explain our genetic results, we consider that the genetic and morphological evidence taken together argue more convincingly towards a case of hybrid speciation. We anticipate that our study will bring attention to this important aspect of reticulate evolution in non-model mammal species. The study of speciation through hybridization is an excellent opportunity to understand the mechanisms leading to speciation in the context of gene flow. PMID:24421898

Hybrid speciation in a marine mammal: the clymene dolphin (Stenella clymene).

PubMed

Amaral, Ana R; Lovewell, Gretchen; Coelho, Maria M; Amato, George; Rosenbaum, Howard C

2014-01-01

Natural hybridization may result in the exchange of genetic material between divergent lineages and even the formation of new taxa. Many of the Neo-Darwinian architects argued that, particularly for animal clades, natural hybridization was maladaptive. Recent evidence, however, has falsified this hypothesis, instead indicating that this process may lead to increased biodiversity through the formation of new species. Although such cases of hybrid speciation have been described in plants, fish and insects, they are considered exceptionally rare in mammals. Here we present evidence for a marine mammal, Stenella clymene, arising through natural hybridization. We found phylogenetic discordance between mitochondrial and nuclear markers, which, coupled with a pattern of transgressive segregation seen in the morphometric variation of some characters, support a case of hybrid speciation. S. clymene is currently genetically differentiated from its putative parental species, Stenella coerueloalba and Stenella longisrostris, although low levels of introgressive hybridization may be occurring. Although non-reticulate forms of evolution, such as incomplete lineage sorting, could explain our genetic results, we consider that the genetic and morphological evidence taken together argue more convincingly towards a case of hybrid speciation. We anticipate that our study will bring attention to this important aspect of reticulate evolution in non-model mammal species. The study of speciation through hybridization is an excellent opportunity to understand the mechanisms leading to speciation in the context of gene flow.

A heuristic approach using multiple criteria for environmentally benign 3PLs selection

NASA Astrophysics Data System (ADS)

Kongar, Elif

2005-11-01

Maintaining competitiveness in an environment where price and quality differences between competing products are disappearing depends on the company's ability to reduce costs and supply time. Timely responses to rapidly changing market conditions require an efficient Supply Chain Management (SCM). Outsourcing logistics to third-party logistics service providers (3PLs) is one commonly used way of increasing the efficiency of logistics operations, while creating a more "core competency focused" business environment. However, this alone may not be sufficient. Due to recent environmental regulations and growing public awareness regarding environmental issues, 3PLs need to be not only efficient but also environmentally benign to maintain companies' competitiveness. Even though an efficient and environmentally benign combination of 3PLs can theoretically be obtained using exhaustive search algorithms, heuristics approaches to the selection process may be superior in terms of the computational complexity. In this paper, a hybrid approach that combines a multiple criteria Genetic Algorithm (GA) with Linear Physical Weighting Algorithm (LPPW) to be used in efficient and environmentally benign 3PLs is proposed. A numerical example is also provided to illustrate the method and the analyses.

Genetic evidence for multiple events of hybridization between wolves and domestic dogs in the Iberian Peninsula.

PubMed

Godinho, Raquel; Llaneza, Luis; Blanco, Juan C; Lopes, Susana; Álvares, Francisco; García, Emilio J; Palacios, Vicente; Cortés, Yolanda; Talegón, Javier; Ferrand, Nuno

2011-12-01

Hybridization between wild species and their domestic counterparts may represent a major threat to natural populations. However, high genetic similarity between the hybridizing taxa makes the detection of hybrids a difficult task and may hinder attempts to assess the impact of hybridization in conservation biology. In this work, we used a combination of 42 autosomal microsatellites together with Y-chromosome microsatellite-defined haplotypes and mtDNA sequences to investigate the occurrence and dynamics of wolf-dog hybridization in the Iberian Peninsula. To do this, we applied a variety of Bayesian analyses and a parallel set of simulation studies to evaluate (i) the differences between Iberian wolves and dogs, (ii) the frequency and geographical distribution of hybridization and (iii) the directionality of hybridization. First, we show that Iberian wolves and dogs form two well-differentiated genetic entities, suggesting that introgressive hybridization is not a widespread phenomenon shaping both gene pools. Second, we found evidence for the existence of hybridization that is apparently restricted to more peripheral and recently expanded wolf populations. Third, we describe compelling evidence suggesting that the dynamics of hybridization in wolf populations is mediated by crosses between male dogs and female wolves. More importantly, the observation of a population showing the occurrence of a continuum of hybrid classes forming mixed packs may indicate that we have underestimated hybridization. If future studies confirm this pattern, then an intriguing avenue of research is to investigate how introgression from free-ranging domestic dogs is enabling wolf populations to adapt to the highly humanized habitats of southern Europe while still maintaining their genetic differentiation. © 2011 Blackwell Publishing Ltd.

Comparison of genetic algorithms with conjugate gradient methods

NASA Technical Reports Server (NTRS)

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

75 FR 2845 - ArborGen, LLC; Availability of an Environmental Assessment for Controlled Release of a...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-19

... Engineered Eucalyptus Hybrid AGENCY: Animal and Plant Health Inspection Service, USDA. ACTION: Notice... for a proposed controlled field release of a genetically engineered clone of a Eucalyptus hybrid. This... proposed controlled field release of a genetically engineered clone of a Eucalyptus hybrid. \\1\\ To view the...

Software For Genetic Algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steve E.

1992-01-01

SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Changes in gene expression between a soybean F1 hybrid and its parents are associated with agronomically valuable traits

USDA-ARS?s Scientific Manuscript database

Plant breeding consists of creating phenotypic and genetic diversity by hybridizing diverse parents and selecting progeny which have new combinations of targeted traits. Soybean [Glycine max (L.) Merr.] genetic diversity is limited because domesticated soybean has undergone multiple genetic bottlene...

Threshold automatic selection hybrid phase unwrapping algorithm for digital holographic microscopy

NASA Astrophysics Data System (ADS)

Zhou, Meiling; Min, Junwei; Yao, Baoli; Yu, Xianghua; Lei, Ming; Yan, Shaohui; Yang, Yanlong; Dan, Dan

2015-01-01

Conventional quality-guided (QG) phase unwrapping algorithm is hard to be applied to digital holographic microscopy because of the long execution time. In this paper, we present a threshold automatic selection hybrid phase unwrapping algorithm that combines the existing QG algorithm and the flood-filled (FF) algorithm to solve this problem. The original wrapped phase map is divided into high- and low-quality sub-maps by selecting a threshold automatically, and then the FF and QG unwrapping algorithms are used in each level to unwrap the phase, respectively. The feasibility of the proposed method is proved by experimental results, and the execution speed is shown to be much faster than that of the original QG unwrapping algorithm.

A genetically distinct hybrid zone occurs for two globally invasive mosquito fish species with striking phenotypic resemblance.

PubMed

Wilk, Rebecca J; Horth, Lisa

2016-12-01

Hybrid zones allow for the investigation of incipient speciation and related evolutionary processes of selection, gene flow, and migration. Interspecific dynamics, like competition, can impact the size, shape, and directional movement of species in hybrid zones. Hybrid zones contribute to a paradox for the biological species concept because interbreeding between species occurs while parental forms remain distinct. A long-standing zone of intergradation or introgression exists for eastern and western mosquito fish ( Gambusia holbrooki and G. affinis ) around Mobile Bay, AL. The region has been studied episodically, over decades, making it perfect for addressing temporal dynamics and for providing a deeper understanding of the genetics of these periodically reclassified fishes (as species or subspecies). We used six microsatellite markers to assess the current population structure and gene flow patterns across 19 populations of mosquito fish and then compared our results with historical data. Genetic evidence demonstrates that the current hybrid zone is located in a similar geographic region as the historical one, even after three decades. Hybrid fish, however, demonstrate relatively low heterozygosity and are genetically distinct from western and eastern mosquito fish populations. Fin ray counts, sometimes used to distinguish the two species from one another, demonstrate more eastern ( G. holbrooki) phenotype fish within the molecular genetic hybrid zone today. Mosquito fish are globally invasive, often found on the leading edge of flooded waters that they colonize, so the impact of hurricanes in the wake of climate change was also evaluated. An increase in the frequency and intensity of hurricanes in the hybrid region has occurred, and this point warrants further attention since hurricanes are known to move these aggressive, invasive species into novel territory. This work contributes to our classical understanding of hybrid zone temporal dynamics, refines our understanding of mosquito fish genetics in their native range, evaluates important genotype-phenotype relationships, and identifies a potential new impact of climate change.

Spatial genetic and morphologic structure of wolves and coyotes in relation to environmental heterogeneity in a Canis hybrid zone.

PubMed

Benson, John F; Patterson, Brent R; Wheeldon, Tyler J

2012-12-01

Eastern wolves have hybridized extensively with coyotes and gray wolves and are listed as a 'species of special concern' in Canada. However, a distinct population of eastern wolves has been identified in Algonquin Provincial Park (APP) in Ontario. Previous studies of the diverse Canis hybrid zone adjacent to APP have not linked genetic analysis with field data to investigate genotype-specific morphology or determine how resident animals of different ancestry are distributed across the landscape in relation to heterogeneous environmental conditions. Accordingly, we studied resident wolves and coyotes in and adjacent to APP to identify distinct Canis types, clarify the extent of the APP eastern wolf population beyond the park boundaries and investigate fine-scale spatial genetic structure and landscape-genotype associations in the hybrid zone. We documented three genetically distinct Canis types within the APP region that also differed morphologically, corresponding to putative gray wolves, eastern wolves and coyotes. We also documented a substantial number of hybrid individuals (36%) that were admixed between 2 or 3 of the Canis types. Breeding eastern wolves were less common outside of APP, but occurred in some unprotected areas where they were sympatric with a diverse combination of coyotes, gray wolves and hybrids. We found significant spatial genetic structure and identified a steep cline extending west from APP where the dominant genotype shifted abruptly from eastern wolves to coyotes and hybrids. The genotypic pattern to the south and northwest was a more complex mosaic of alternating genotypes. We modelled genetic ancestry in response to prey availability and human disturbance and found that individuals with greater wolf ancestry occupied areas of higher moose density and fewer roads. Our results clarify the structure of the Canis hybrid zone adjacent to APP and provide unique insight into environmental conditions influencing hybridization dynamics between wolves and coyotes. © 2012 Blackwell Publishing Ltd.

Genetic loci with parent-of-origin effects cause hybrid seed lethality in crosses between Mimulus species.

PubMed

Garner, Austin G; Kenney, Amanda M; Fishman, Lila; Sweigart, Andrea L

2016-07-01

In flowering plants, F1 hybrid seed lethality is a common outcome of crosses between closely related diploid species, but the genetic basis of this early-acting and potentially widespread form of postzygotic reproductive isolation is largely unknown. We intercrossed two closely related species of monkeyflower, Mimulus guttatus and Mimulus tilingii, to characterize the mechanisms and strength of postzygotic reproductive isolation. Then, using a reciprocal backcross design, we performed high-resolution genetic mapping to determine the genetic architecture of hybrid seed lethality and directly test for loci with parent-of-origin effects. We found that F1 hybrid seed lethality is an exceptionally strong isolating barrier between Mimulus species, with reciprocal crosses producing < 1% viable seeds. This form of postzygotic reproductive isolation appears to be highly polygenic, indicating that multiple incompatibility loci have accumulated rapidly between these closely related Mimulus species. It is also primarily caused by genetic loci with parent-of-origin effects, suggesting a possible role for imprinted genes in the evolution of Mimulus hybrid seed lethality. Our findings suggest that divergence in loci with parent-of-origin effects, which is probably driven by genomic coevolution within lineages, might be an important source of hybrid incompatibilities between flowering plant species. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Genotypic and phenotypic evaluation of off-type grasses in hybrid Bermudagrass [Cynodon dactylon (L.) Pers. x C. transvaalensis Burtt-Davy] putting greens using genotyping-by-sequencing and morphological characterization.

PubMed

Reasor, Eric H; Brosnan, James T; Staton, Margaret E; Lane, Thomas; Trigiano, Robert N; Wadl, Phillip A; Conner, Joann A; Schwartz, Brian M

2018-01-01

Interspecific hybrid bermudagrass [ Cynodon dactylon (L.) Pers. x C. transvaalensis Burtt-Davy] is one of the most widely used grasses on golf courses, with cultivars derived from 'Tifgreen' or 'Tifdwarf' particularly used for putting greens. Many bermudagrass cultivars established for putting greens can be genetically unstable and lead to the occurrence of undesirable off-type grasses that vary in phenotype. The objective of this research was to genetically and phenotypically differentiate off-type grasses and hybrid cultivars. Beginning in 2013, off-type and desirable hybrid bermudagrass samples were collected from golf course putting greens in the southeastern United States and genetically and phenotypically characterized using genotyping-by-sequencing and morphology. Genotyping-by-sequencing determined that 11% (5) of off-type and desirable samples from putting greens were genetically divergent from standard cultivars such as Champion, MiniVerde, Tifdwarf, TifEagle, and Tifgreen. In addition, genotyping-by-sequencing was unable to genetically distinguish all standard cultivars from one another due to their similar origin and clonal propagation; however, over 90,000 potentially informative nucleotide variants were identified among the triploid hybrid cultivars. Although few genetic differences were found in this research, samples harvested from golf course putting greens had variable morphology and were clustered into three distinct phenotypic groups. The majority of off-type grasses in hybrid bermudagrass putting greens were genetically similar with variable morphological traits. Off-type grasses within golf course putting greens have the potential to compromise putting surface functionality and aesthetics.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

PubMed

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-05-09

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

Non-preconditioned conjugate gradient on cell and FPGA based hybrid supercomputer nodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dubois, David H; Dubois, Andrew J; Boorman, Thomas M

2009-01-01

This work presents a detailed implementation of a double precision, non-preconditioned, Conjugate Gradient algorithm on a Roadrunner heterogeneous supercomputer node. These nodes utilize the Cell Broadband Engine Architecture{sup TM} in conjunction with x86 Opteron{sup TM} processors from AMD. We implement a common Conjugate Gradient algorithm, on a variety of systems, to compare and contrast performance. Implementation results are presented for the Roadrunner hybrid supercomputer, SRC Computers, Inc. MAPStation SRC-6 FPGA enhanced hybrid supercomputer, and AMD Opteron only. In all hybrid implementations wall clock time is measured, including all transfer overhead and compute timings.

Non-preconditioned conjugate gradient on cell and FPCA-based hybrid supercomputer nodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dubois, David H; Dubois, Andrew J; Boorman, Thomas M

2009-03-10

This work presents a detailed implementation of a double precision, Non-Preconditioned, Conjugate Gradient algorithm on a Roadrunner heterogeneous supercomputer node. These nodes utilize the Cell Broadband Engine Architecture{trademark} in conjunction with x86 Opteron{trademark} processors from AMD. We implement a common Conjugate Gradient algorithm, on a variety of systems, to compare and contrast performance. Implementation results are presented for the Roadrunner hybrid supercomputer, SRC Computers, Inc. MAPStation SRC-6 FPGA enhanced hybrid supercomputer, and AMD Opteron only. In all hybrid implementations wall clock time is measured, including all transfer overhead and compute timings.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

PubMed

Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

2014-12-01

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.

Rapid genetic and epigenetic alterations under intergeneric genomic shock in newly synthesized Chrysanthemum morifolium x Leucanthemum paludosum hybrids (Asteraceae).

PubMed

Wang, Haibin; Jiang, Jiafu; Chen, Sumei; Qi, Xiangyu; Fang, Weimin; Guan, Zhiyong; Teng, Nianjun; Liao, Yuan; Chen, Fadi

2014-01-01

The Asteraceae family is at the forefront of the evolution due to frequent hybridization. Hybridization is associated with the induction of widespread genetic and epigenetic changes and has played an important role in the evolution of many plant taxa. We attempted the intergeneric cross Chrysanthemum morifolium × Leucanthemum paludosum. To obtain the success in cross, we have to turn to ovule rescue. DNA profiling of the amphihaploid and amphidiploid was investigated using amplified fragment length polymorphism, sequence-related amplified polymorphism, start codon targeted polymorphism, and methylation-sensitive amplification polymorphism (MSAP). Hybridization induced rapid changes at the genetic and the epigenetic levels. The genetic changes mainly involved loss of parental fragments and gaining of novel fragments, and some eliminated sequences possibly from the noncoding region of L. paludosum. The MSAP analysis indicated that the level of DNA methylation was lower in the amphiploid (∼45%) than in the parental lines (51.5-50.6%), whereas it increased after amphidiploid formation. Events associated with intergeneric genomic shock were a feature of C. morifolium × L. paludosum hybrid, given that the genetic relationship between the parental species is relatively distant. Our results provide genetic and epigenetic evidence for understanding genomic shock in wide crosses between species in Asteraceae and suggest a need to expand our current evolutionary framework to encompass a genetic/epigenetic dimension when seeking to understand wide crosses.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

A fast complex integer convolution using a hybrid transform

NASA Technical Reports Server (NTRS)

Reed, I. S.; K Truong, T.

1978-01-01

It is shown that the Winograd transform can be combined with a complex integer transform over the Galois field GF(q-squared) to yield a new algorithm for computing the discrete cyclic convolution of complex number points. By this means a fast method for accurately computing the cyclic convolution of a sequence of complex numbers for long convolution lengths can be obtained. This new hybrid algorithm requires fewer multiplications than previous algorithms.

The Evolution of Polymorphic Hybrid Incompatibilities in House Mice.

PubMed

Larson, Erica L; Vanderpool, Dan; Sarver, Brice A J; Callahan, Colin; Keeble, Sara; Provencio, Lorraine P; Kessler, Michael D; Stewart, Vanessa; Nordquist, Erin; Dean, Matthew D; Good, Jeffrey M

2018-04-24

Resolving the mechanistic and genetic bases of reproductive barriers between species is essential to understanding the evolutionary forces that shape speciation. Intrinsic hybrid incompatibilities are often treated as fixed between species, yet there can be considerable variation in the strength of reproductive isolation between populations. The extent and causes of this variation remain poorly understood in most systems. We investigated the genetic basis of variable hybrid male sterility (HMS) between two recently diverged subspecies of house mice, Mus musculus domesticus and M. m. musculus We found that polymorphic HMS has a surprisingly complex genetic basis, with contributions from at least five autosomal loci segregating between two closely related wild-derived strains of M. m. musculus One of the HMS-linked regions on Chromosome 4 also showed extensive introgression among inbred laboratory strains and transmission ratio distortion (TRD) in hybrid crosses. Using additional crosses and whole genome sequencing of sperm pools, we showed that TRD was limited to hybrid crosses and was not due to differences in sperm motility between M. m. musculus strains. Based on these results, we argue that TRD likely reflects additional incompatibilities that reduce hybrid embryonic viability. In some common inbred strains of mice, selection against deleterious interactions appears to have unexpectedly driven introgression at loci involved in epistatic hybrid incompatibilities. The highly variable genetic basis to F1 hybrid incompatibilities between closely related mouse lineages argues that a thorough dissection of reproductive isolation will require much more extensive sampling of natural variation than has been commonly utilized in mice and other model systems. Copyright © 2018, Genetics.

An Efficient Rank Based Approach for Closest String and Closest Substring

PubMed Central

2012-01-01

This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results. PMID:22675483

Genetic analysis shows low levels of hybridization between African wildcats (Felis silvestris lybica) and domestic cats (F. s. catus) in South Africa

PubMed Central

Le Roux, Johannes J; Foxcroft, Llewellyn C; Herbst, Marna; MacFadyen, Sandra

2015-01-01

Hybridization between domestic and wild animals is a major concern for biodiversity conservation, and as habitats become increasingly fragmented, conserving biodiversity at all levels, including genetic, becomes increasingly important. Except for tropical forests and true deserts, African wildcats occur across the African continent; however, almost no work has been carried out to assess its genetic status and extent of hybridization with domestic cats. For example, in South Africa it has been argued that the long-term viability of maintaining pure wildcat populations lies in large protected areas only, isolated from human populations. Two of the largest protected areas in Africa, the Kgalagadi Transfrontier and Kruger National Parks, as well as the size of South Africa and range of landscape uses, provide a model situation to assess how habitat fragmentation and heterogeneity influences the genetic purity of African wildcats. Using population genetic and home range data, we examined the genetic purity of African wildcats and their suspected hybrids across South Africa, including areas within and outside of protected areas. Overall, we found African wildcat populations to be genetically relatively pure, but instances of hybridization and a significant relationship between the genetic distinctiveness (purity) of wildcats and human population pressure were evident. The genetically purest African wildcats were found in the Kgalagadi Transfrontier Park, while samples from around Kruger National Park showed cause for concern, especially combined with the substantial human population density along the park's boundary. While African wildcat populations in South Africa generally appear to be genetically pure, with low levels of hybridization, our genetic data do suggest that protected areas may play an important role in maintaining genetic purity by reducing the likelihood of contact with domestic cats. We suggest that approaches such as corridors between protected areas are unlikely to remain effective for wildcat conservation, as the proximity to human settlements around these areas is projected to increase the wild/domestic animal interface. Thus, large, isolated protected areas will become increasingly important for wildcat conservation and efforts need to be made to prevent introduction of domestic cats into these areas. PMID:25691958

Genetic analysis shows low levels of hybridization between African wildcats (Felis silvestris lybica) and domestic cats (F. s. catus) in South Africa.

PubMed

Le Roux, Johannes J; Foxcroft, Llewellyn C; Herbst, Marna; MacFadyen, Sandra

2015-01-01

Hybridization between domestic and wild animals is a major concern for biodiversity conservation, and as habitats become increasingly fragmented, conserving biodiversity at all levels, including genetic, becomes increasingly important. Except for tropical forests and true deserts, African wildcats occur across the African continent; however, almost no work has been carried out to assess its genetic status and extent of hybridization with domestic cats. For example, in South Africa it has been argued that the long-term viability of maintaining pure wildcat populations lies in large protected areas only, isolated from human populations. Two of the largest protected areas in Africa, the Kgalagadi Transfrontier and Kruger National Parks, as well as the size of South Africa and range of landscape uses, provide a model situation to assess how habitat fragmentation and heterogeneity influences the genetic purity of African wildcats. Using population genetic and home range data, we examined the genetic purity of African wildcats and their suspected hybrids across South Africa, including areas within and outside of protected areas. Overall, we found African wildcat populations to be genetically relatively pure, but instances of hybridization and a significant relationship between the genetic distinctiveness (purity) of wildcats and human population pressure were evident. The genetically purest African wildcats were found in the Kgalagadi Transfrontier Park, while samples from around Kruger National Park showed cause for concern, especially combined with the substantial human population density along the park's boundary. While African wildcat populations in South Africa generally appear to be genetically pure, with low levels of hybridization, our genetic data do suggest that protected areas may play an important role in maintaining genetic purity by reducing the likelihood of contact with domestic cats. We suggest that approaches such as corridors between protected areas are unlikely to remain effective for wildcat conservation, as the proximity to human settlements around these areas is projected to increase the wild/domestic animal interface. Thus, large, isolated protected areas will become increasingly important for wildcat conservation and efforts need to be made to prevent introduction of domestic cats into these areas.

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

NASA Technical Reports Server (NTRS)

Burchett, Bradley T.

2003-01-01

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

PubMed

Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui

2017-01-01

To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.

A web server for mining Comparative Genomic Hybridization (CGH) data

NASA Astrophysics Data System (ADS)

Liu, Jun; Ranka, Sanjay; Kahveci, Tamer

2007-11-01

Advances in cytogenetics and molecular biology has established that chromosomal alterations are critical in the pathogenesis of human cancer. Recurrent chromosomal alterations provide cytological and molecular markers for the diagnosis and prognosis of disease. They also facilitate the identification of genes that are important in carcinogenesis, which in the future may help in the development of targeted therapy. A large amount of publicly available cancer genetic data is now available and it is growing. There is a need for public domain tools that allow users to analyze their data and visualize the results. This chapter describes a web based software tool that will allow researchers to analyze and visualize Comparative Genomic Hybridization (CGH) datasets. It employs novel data mining methodologies for clustering and classification of CGH datasets as well as algorithms for identifying important markers (small set of genomic intervals with aberrations) that are potentially cancer signatures. The developed software will help in understanding the relationships between genomic aberrations and cancer types.

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Implementation of an effective hybrid GA for large-scale traveling salesman problems.

PubMed

Nguyen, Hung Dinh; Yoshihara, Ikuo; Yamamori, Kunihito; Yasunaga, Moritoshi

2007-02-01

This correspondence describes a hybrid genetic algorithm (GA) to find high-quality solutions for the traveling salesman problem (TSP). The proposed method is based on a parallel implementation of a multipopulation steady-state GA involving local search heuristics. It uses a variant of the maximal preservative crossover and the double-bridge move mutation. An effective implementation of the Lin-Kernighan heuristic (LK) is incorporated into the method to compensate for the GA's lack of local search ability. The method is validated by comparing it with the LK-Helsgaun method (LKH), which is one of the most effective methods for the TSP. Experimental results with benchmarks having up to 316228 cities show that the proposed method works more effectively and efficiently than LKH when solving large-scale problems. Finally, the method is used together with the implementation of the iterated LK to find a new best tour (as of June 2, 2003) for a 1904711-city TSP challenge.

Hybrid-dual-fourier tomographic algorithm for a fast three-dimensionial optical image reconstruction in turbid media

NASA Technical Reports Server (NTRS)

Alfano, Robert R. (Inventor); Cai, Wei (Inventor)

2007-01-01

A reconstruction technique for reducing computation burden in the 3D image processes, wherein the reconstruction procedure comprises an inverse and a forward model. The inverse model uses a hybrid dual Fourier algorithm that combines a 2D Fourier inversion with a 1D matrix inversion to thereby provide high-speed inverse computations. The inverse algorithm uses a hybrid transfer to provide fast Fourier inversion for data of multiple sources and multiple detectors. The forward model is based on an analytical cumulant solution of a radiative transfer equation. The accurate analytical form of the solution to the radiative transfer equation provides an efficient formalism for fast computation of the forward model.

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications

PubMed Central

2012-01-01

Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626

Genomic islands of differentiation in two songbird species reveal candidate genes for hybrid female sterility.

PubMed

Mořkovský, Libor; Janoušek, Václav; Reif, Jiří; Rídl, Jakub; Pačes, Jan; Choleva, Lukáš; Janko, Karel; Nachman, Michael W; Reifová, Radka

2018-02-01

Hybrid sterility is a common first step in the evolution of postzygotic reproductive isolation. According to Haldane's Rule, it affects predominantly the heterogametic sex. While the genetic basis of hybrid male sterility in organisms with heterogametic males has been studied for decades, the genetic basis of hybrid female sterility in organisms with heterogametic females has received much less attention. We investigated the genetic basis of reproductive isolation in two closely related avian species, the common nightingale (Luscinia megarhynchos) and the thrush nightingale (L. luscinia), that hybridize in a secondary contact zone and produce viable hybrid progeny. In accordance with Haldane's Rule, hybrid females are sterile, while hybrid males are fertile, allowing gene flow to occur between the species. Using transcriptomic data from multiple individuals of both nightingale species, we identified genomic islands of high differentiation (F ST ) and of high divergence (D xy ), and we analysed gene content and patterns of molecular evolution within these islands. Interestingly, we found that these islands were enriched for genes related to female meiosis and metabolism. The islands of high differentiation and divergence were also characterized by higher levels of linkage disequilibrium than the rest of the genome in both species indicating that they might be situated in genomic regions of low recombination. This study provides one of the first insights into genetic basis of hybrid female sterility in organisms with heterogametic females. © 2018 John Wiley & Sons Ltd.

Learning Intelligent Genetic Algorithms Using Japanese Nonograms

ERIC Educational Resources Information Center

Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

2012-01-01

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

Natural and Anthropogenic Hybridization in Two Species of Eastern Brazilian Marmosets (Callithrix jacchus and C. penicillata)

PubMed Central

Malukiewicz, Joanna; Boere, Vanner; Fuzessy, Lisieux F.; Grativol, Adriana D.; de Oliveira e Silva, Ita; Pereira, Luiz C. M.; Ruiz-Miranda, Carlos R.; Valença, Yuri M.; Stone, Anne C.

2015-01-01

Animal hybridization is well documented, but evolutionary outcomes and conservation priorities often differ for natural and anthropogenic hybrids. Among primates, an order with many endangered species, the two contexts can be hard to disentangle from one another, which carries important conservation implications. Callithrix marmosets give us a unique glimpse of genetic hybridization effects under distinct natural and human-induced contexts. Here, we use a 44 autosomal microsatellite marker panel to examine genome-wide admixture levels and introgression at a natural C. jacchus and C. penicillata species border along the São Francisco River in NE Brazil and in an area of Rio de Janeiro state where humans introduced these species exotically. Additionally, we describe for the first time autosomal genetic diversity in wild C. penicillata and expand previous C. jacchus genetic data. We characterize admixture within the natural zone as bimodal where hybrid ancestry is biased toward one parental species or the other. We also show evidence that São Francisco River islands are gateways for bidirectional gene flow across the species border. In the anthropogenic zone, marmosets essentially form a hybrid swarm with intermediate levels of admixture, likely from the absence of strong physical barriers to interspecific breeding. Our data show that while hybridization can occur naturally, the presence of physical, even if leaky, barriers to hybridization is important for maintaining species genetic integrity. Thus, we suggest further study of hybridization under different contexts to set well informed conservation guidelines for hybrid populations that often fit somewhere between “natural” and “man-made.” PMID:26061111

Distant hybridization leads to different ploidy fishes.

PubMed

Liu, ShaoJun

2010-04-01

Distant hybridization makes it possible to transfer the genome of one species to another, which results in changes in phenotypes and genotypes of the progenies. This study shows that distant hybridization or the combination of this method with gynogenesis or androgenesis lead to different ploidy fishes with genetic variation, including fertile tetraploid hybrids, sterile triploid hybrids, fertile diploid hybrids, fertile diploid gynogenetic fish, and their derived progenies. The formations of the different ploidy fishes depend on the genetic relationship between the parents. In this study, several types of distant hybridization, including red crucian carp (Carassius auratus red var.) (2n=100, abbreviated as RCC) (female) x common carp (Cyprinus carpio L.) (2n=100, abbreviated as CC) (male), and RCC (2n=100) (female) x blunt snout bream (Megalobrama amblycephala) (2n=48, abbreviated as BSB) (male) are described. In the distant hybridization of RCC (female) x CC (male), bisexual fertile F(3)-F(18) allotetraploid hybrids (4n=200, abbreviated as 4nAT) were formed. The diploid hybrid eggs and diploid sperm generated by the females and males of 4nAT developed into diploid gynogenetic hybrids and diploid androgenetic hybrids, respectively, by gynogenesis and androgenesis, without treatment for doubling the chromosome. Improved tetraploid hybrids and improved diploid fishes with genetic variation were derived from the gynogenetic hybrid line. The improved diploid fishes included the high-body RCC and high-body goldfish. The formation of the tetraploid hybrids was related to the occurrence of unreduced gametes generated from the diploid hybrids, which involved in premeiotic endoreduplication, endomitosis, or fusion of germ cells. The sterile triploid hybrids (3n=150) were produced on a large scale by crossing the males of tetraploid hybrids with females of diploid fish (2n=100). In another distant hybridization of RCC (female) x BSB (male), different ploidy fishes were obtained, including diploid bisexual fertile natural gynogenetic fish (2n=100), sterile triploid hybrids (3n=124), and bisexual fertile tetraploid hybrids (4n=148). Furthermore, two kinds of pentaploid hybrids (5n=172 and 5n=198) were formed. The biological characteristics and the mechanisms of formation of the different ploidy fish were compared and discussed at the cellular and molecular level. The results indicated distant hybridization or the combination of this method with gynogenesis or androgenesis affects the formation of different ploidy fish with genetic variation.

Predicting DNA hybridization kinetics from sequence

NASA Astrophysics Data System (ADS)

Zhang, Jinny X.; Fang, John Z.; Duan, Wei; Wu, Lucia R.; Zhang, Angela W.; Dalchau, Neil; Yordanov, Boyan; Petersen, Rasmus; Phillips, Andrew; Zhang, David Yu

2018-01-01

Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.

Comparative genetics of hybrid incompatibility: sterility in two Solanum species crosses.

PubMed

Moyle, Leonie C; Nakazato, Takuya

2008-07-01

The genetic basis of hybrid sterility can provide insight into the genetic and evolutionary origins of species barriers. We examine the genetics of hybrid incompatibility between two diploid plant species in the plant clade Solanum sect. Lycopersicon. Using a set of near-isogenic lines (NILs) representing the wild species Solanum pennellii (formerly Lycopersicon pennellii) in the genetic background of the cultivated tomato S. lycopersicum (formerly L. esculentum), we found that hybrid pollen and seed infertility are each based on a modest number of loci, male (pollen) and other (seed) incompatibility factors are roughly comparable in number, and seed-infertility QTL act additively or recessively. These findings are remarkably consistent with our previous analysis in a different species pair, S. lycopersicum x S. habrochaites. Data from both studies contrast strongly with data from Drosophila. Finally, QTL for pollen and seed sterility from the two Solanum studies were chromosomally colocalized, indicating a shared evolutionary history for these QTL, a nonrandom genomic distribution of loci causing sterility, and/or a proclivity of certain genes to be involved in hybrid sterility. We show that comparative mapping data can delimit the probable timing of evolution of detected QTL and discern which sterility loci likely evolved earliest among species.

Improving personalized link prediction by hybrid diffusion

NASA Astrophysics Data System (ADS)

Liu, Jin-Hu; Zhu, Yu-Xiao; Zhou, Tao

2016-04-01

Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which each individual will get equal number of diversiform predictions. While the performances of many classical algorithms are not satisfactory under this framework, thus new algorithms are in urgent need. Motivated by previous researches in other fields, we generalize heat conduction process to the framework of personalized link prediction and find that this method outperforms many classical similarity-based algorithms, especially in the performance of diversity. In addition, we demonstrate that adding one ground node that is supposed to connect all the nodes in the system will greatly benefit the performance of heat conduction. Finally, better hybrid algorithms composed of local random walk and heat conduction have been proposed. Numerical results show that the hybrid algorithms can outperform other algorithms simultaneously in all four adopted metrics: AUC, precision, recall and hamming distance. In a word, this work may shed some light on the in-depth understanding of the effect of physical processes in personalized link prediction.

Genetic Diversity, Rather than Cultivar Type, Determines Relative Grain Cd Accumulation in Hybrid Rice

PubMed Central

Sun, Liang; Xu, Xiaxu; Jiang, Youru; Zhu, Qihong; Yang, Fei; Zhou, Jieqiang; Yang, Yuanzhu; Huang, Zhiyuan; Li, Aihong; Chen, Lianghui; Tang, Wenbang; Zhang, Guoyu; Wang, Jiurong; Xiao, Guoying; Huang, Daoyou; Chen, Caiyan

2016-01-01

Cadmium (Cd) is a toxic element, and rice is known to be a leading source of dietary Cd for people who consume rice as their main caloric resource. Hybrid rice has dominated rice production in southern China and has been adopted worldwide. The characteristics of high yield heterosis of rice hybrids makes the public think intuitively that the hybrid rice accumulates more Cd in grain than do inbred cultivars. A detailed understanding of the genetic basis of grain Cd accumulation in hybrids and developing Cd-safe rice are one of the top priorities for hybrid rice breeders at present. In this study, we investigated genetic diversity and grain Cd levels in 617 elite rice hybrids collected from the middle and lower Yangtze River Valley in China and 68 inbred cultivars from around the world. We found that there are large variations in grain Cd accumulation in both the hybrids and their inbred counterparts. However, we found grain Cd levels in the rice hybrids to be similar to the levels in indica rice inbreds, suggesting that the hybrids do not accumulate more Cd than do the inbred rice cultivars. Further analysis revealed that the high heritability of Cd accumulation in the grain and the single indica population structure increases the risk of Cd over-accumulation in hybrid rice. The genetic effects of Cd-related QTLs, which have been identified in related Cd-QTL mapping studies, were also determined in the hybrid rice population. Four QTLs were identified as being associated with the variation in grain Cd levels; three of these loci exhibited obvious indica-japonica differentiations. Our study will provide a better understanding of grain Cd accumulations in hybrid rice, and pave the way toward effective breeding for high-yielding, low grain-Cd hybrids in the future. PMID:27708659

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.

PubMed

Amudha, P; Karthik, S; Sivakumari, S

2015-01-01

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

PubMed Central

Amudha, P.; Karthik, S.; Sivakumari, S.

2015-01-01

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625

Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Wu, Baodong; Li, Shigang; Zhang, Yunquan; Nie, Ningming

2017-02-01

The parallel Kinetic Monte Carlo (KMC) algorithm based on domain decomposition has been widely used in large-scale physical simulations. However, the communication overhead of the parallel KMC algorithm is critical, and severely degrades the overall performance and scalability. In this paper, we present a hybrid optimization strategy to reduce the communication overhead for the parallel KMC simulations. We first propose a communication aggregation algorithm to reduce the total number of messages and eliminate the communication redundancy. Then, we utilize the shared memory to reduce the memory copy overhead of the intra-node communication. Finally, we optimize the communication scheduling using the neighborhood collective operations. We demonstrate the scalability and high performance of our hybrid optimization strategy by both theoretical and experimental analysis. Results show that the optimized KMC algorithm exhibits better performance and scalability than the well-known open-source library-SPPARKS. On 32-node Xeon E5-2680 cluster (total 640 cores), the optimized algorithm reduces the communication time by 24.8% compared with SPPARKS.

Information Filtering via a Scaling-Based Function

PubMed Central

Qiu, Tian; Zhang, Zi-Ke; Chen, Guang

2013-01-01

Finding a universal description of the algorithm optimization is one of the key challenges in personalized recommendation. In this article, for the first time, we introduce a scaling-based algorithm (SCL) independent of recommendation list length based on a hybrid algorithm of heat conduction and mass diffusion, by finding out the scaling function for the tunable parameter and object average degree. The optimal value of the tunable parameter can be abstracted from the scaling function, which is heterogeneous for the individual object. Experimental results obtained from three real datasets, Netflix, MovieLens and RYM, show that the SCL is highly accurate in recommendation. More importantly, compared with a number of excellent algorithms, including the mass diffusion method, the original hybrid method, and even an improved version of the hybrid method, the SCL algorithm remarkably promotes the personalized recommendation in three other aspects: solving the accuracy-diversity dilemma, presenting a high novelty, and solving the key challenge of cold start problem. PMID:23696829

Boiler-turbine control system design using a genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Finding minimum spanning trees more efficiently for tile-based phase unwrapping

NASA Astrophysics Data System (ADS)

Sawaf, Firas; Tatam, Ralph P.

2006-06-01

The tile-based phase unwrapping method employs an algorithm for finding the minimum spanning tree (MST) in each tile. We first examine the properties of a tile's representation from a graph theory viewpoint, observing that it is possible to make use of a more efficient class of MST algorithms. We then describe a novel linear time algorithm which reduces the size of the MST problem by half at the least, and solves it completely at best. We also show how this algorithm can be applied to a tile using a sliding window technique. Finally, we show how the reduction algorithm can be combined with any other standard MST algorithm to achieve a more efficient hybrid, using Prim's algorithm for empirical comparison and noting that the reduction algorithm takes only 0.1% of the time taken by the overall hybrid.

Hybrid approach of selecting hyperparameters of support vector machine for regression.

PubMed

Jeng, Jin-Tsong

2006-06-01

To select the hyperparameters of the support vector machine for regression (SVR), a hybrid approach is proposed to determine the kernel parameter of the Gaussian kernel function and the epsilon value of Vapnik's epsilon-insensitive loss function. The proposed hybrid approach includes a competitive agglomeration (CA) clustering algorithm and a repeated SVR (RSVR) approach. Since the CA clustering algorithm is used to find the nearly "optimal" number of clusters and the centers of clusters in the clustering process, the CA clustering algorithm is applied to select the Gaussian kernel parameter. Additionally, an RSVR approach that relies on the standard deviation of a training error is proposed to obtain an epsilon in the loss function. Finally, two functions, one real data set (i.e., a time series of quarterly unemployment rate for West Germany) and an identification of nonlinear plant are used to verify the usefulness of the hybrid approach.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2017-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2016-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered-reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.

PubMed

Li, Shan; Kang, Liying; Zhao, Xing-Ming

2014-01-01

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

Genetic identification of spotted owls, barred owls, and their hybrids: Legal implications of hybrid identity

USGS Publications Warehouse

Haig, Susan M.; Wennerberg, Liv; Mullins, Thomas D.; Forsman, E.D.; Trail, P.

2004-01-01

Recent population expansion of Barred Owls ( Strix varia) into western North America has led to concern that they may compete with and further harm the Northern Spotted Owl ( S. occidentalis caurina), which is already listed as threatened under the U.S. Endangered Species Act (ESA). Because they hybridize, there is a legal need under the ESA for forensic identification of both species and their hybrids. We used mitochondrial control-region DNA and amplified fragment-length polymorphism (AFLP) analyses to assess maternal and biparental gene flow in this hybridization process. Mitochondrial DNA sequences (524 base pairs) indicated large divergence between Barred and Spotted Owls (13.9%). Further, the species formed two distinct clades with no signs of previous introgression. Fourteen diagnostic AFLP bands also indicated extensive divergence between the species, including markers differentiating them. Principal coordinate analyses and assignment tests clearly supported this differentiation. We found that hybrids had unique genetic combinations, including AFLP markers from both parental species, and identified known hybrids as well as potential hybrids with unclear taxonomic status. Our analyses corroborated the findings of extensive field studies that most hybrids genetically sampled resulted from crosses between female Barred Owls and male Spotted Owls. These genetic markers make it possible to clearly identify these species as well as hybrids and can now be used for research, conservation, and law enforcement. Several legal avenues may facilitate future conservation of Spotted Owls and other ESA-listed species that hybridize, including the ESA similarity-of-appearance clause (section 4[e]) and the Migratory Bird Treaty Act. The Migratory Bird Treaty Act appears to be the most useful route at this time.

Comparative assessment of genetic and morphological variation at an extensive hybrid zone between two wild cats in southern Brazil.

PubMed

Trigo, Tatiane C; Tirelli, Flávia P; de Freitas, Thales R O; Eizirik, Eduardo

2014-01-01

Increased attention towards the Neotropical cats Leopardus guttulus and L. geoffroyi was prompted after genetic studies identified the occurrence of extensive hybridization between them at their geographic contact zone in southern Brazil. This is a region where two biomes intersect, each of which is associated with one of the hybridizing species (Atlantic Forest with L. guttulus and Pampas with L. geoffroyi). In this study, we conducted in-depth analyses of multiple molecular markers aiming to characterize the magnitude and spatial structure of this hybrid zone. We also performed a morphological assessment of these species, aiming to test their phenotypic differentiation at the contact zone, as well as the correlation between morphological features and the admixture status of the individuals. We found strong evidence for extensive and complex hybridization, with at least 40% of the individuals sampled in Rio Grande do Sul state (southernmost Brazil) identified as hybrids resulting from post-F1 generations. Despite such a high level of hybridization, samples collected in this state still comprised two recognizable clusters (genetically and morphologically). Genetically pure individuals were sampled mainly in regions farther from the contact zone, while hybrids concentrated in a central region (exactly at the interface between the two biomes). The morphological data set also revealed a strong spatial structure, which was correlated with the molecular results but displayed an even more marked separation between the clusters. Hybrids often did not present intermediate body sizes and could not be clearly distinguished morphologically from the parental forms. This observation suggests that some selective pressure may be acting on the hybrids, limiting their dispersal away from the hybrid zone and perhaps favoring genomic combinations that maintain adaptive phenotypic features of one or the other parental species.

Comparative Assessment of Genetic and Morphological Variation at an Extensive Hybrid Zone between Two Wild Cats in Southern Brazil

PubMed Central

Trigo, Tatiane C.; Tirelli, Flávia P.; de Freitas, Thales R. O.; Eizirik, Eduardo

2014-01-01

Increased attention towards the Neotropical cats Leopardus guttulus and L. geoffroyi was prompted after genetic studies identified the occurrence of extensive hybridization between them at their geographic contact zone in southern Brazil. This is a region where two biomes intersect, each of which is associated with one of the hybridizing species (Atlantic Forest with L. guttulus and Pampas with L. geoffroyi). In this study, we conducted in-depth analyses of multiple molecular markers aiming to characterize the magnitude and spatial structure of this hybrid zone. We also performed a morphological assessment of these species, aiming to test their phenotypic differentiation at the contact zone, as well as the correlation between morphological features and the admixture status of the individuals. We found strong evidence for extensive and complex hybridization, with at least 40% of the individuals sampled in Rio Grande do Sul state (southernmost Brazil) identified as hybrids resulting from post-F1 generations. Despite such a high level of hybridization, samples collected in this state still comprised two recognizable clusters (genetically and morphologically). Genetically pure individuals were sampled mainly in regions farther from the contact zone, while hybrids concentrated in a central region (exactly at the interface between the two biomes). The morphological data set also revealed a strong spatial structure, which was correlated with the molecular results but displayed an even more marked separation between the clusters. Hybrids often did not present intermediate body sizes and could not be clearly distinguished morphologically from the parental forms. This observation suggests that some selective pressure may be acting on the hybrids, limiting their dispersal away from the hybrid zone and perhaps favoring genomic combinations that maintain adaptive phenotypic features of one or the other parental species. PMID:25250657

Migratory orientation in a narrow avian hybrid zone

PubMed Central

Toews, David P.L.; Delmore, Kira E.; Osmond, Matthew M.; Taylor, Philip D.

2017-01-01

Background Zones of contact between closely related taxa with divergent migratory routes, termed migratory divides, have been suggested as areas where hybrid offspring may have intermediate and inferior migratory routes, resulting in low fitness of hybrids and thereby promoting speciation. In the Rocky Mountains of Canada there is a narrow hybrid zone between Audubon’s and myrtle warblers that is likely maintained by selection against hybrids. Band recoveries and isotopic studies indicate that this hybrid zone broadly corresponds to the location of a possible migratory divide, with Audubon’s warblers migrating south-southwest and myrtle warblers migrating southeast. We tested a key prediction of the migratory divide hypothesis: that genetic background would be predictive of migratory orientation among warblers in the center of the hybrid zone. Methods We recorded fall migratory orientation of wild-caught migrating warblers in the center of the hybrid zone as measured by video-based monitoring of migratory restlessness in circular orientation chambers. We then tested whether there was a relationship between migratory orientation and genetic background, as measured using a set of species-specific diagnostic genetic markers. Results We did not detect a significant association between orientation and genetic background. There was large variation among individuals in orientation direction. Mean orientation was towards the NE, surprising for birds on fall migration, but aligned with the mountain valley in which the study took place. Conclusions Only one other study has directly analyzed migratory orientation among naturally-produced hybrids in a migratory divide. While the other study showed an association between genetic background and orientation, we did not observe such an association in yellow-rumped warblers. We discuss possible reasons, including the possibility of a lack of a strong migratory divide in this hybrid zone and/or methodological limitations that may have prevented accurate measurements of long-distance migratory orientation. PMID:28439469

The variable genomic architecture of isolation between hybridizing species of house mice.

PubMed

Teeter, Katherine C; Thibodeau, Lisa M; Gompert, Zachariah; Buerkle, C Alex; Nachman, Michael W; Tucker, Priscilla K

2010-02-01

Studies of the genetics of hybrid zones can provide insight into the genomic architecture of species boundaries. By examining patterns of introgression of multiple loci across a hybrid zone, it may be possible to identify regions of the genome that have experienced selection. Here, we present a comparison of introgression in two replicate transects through the house mouse hybrid zone through central Europe, using data from 41 single nucleotide markers. Using both genomic and geographic clines, we found many differences in patterns of introgression between the two transects, as well as some similarities. We found that many loci may have experienced the effects of selection at linked sites, including selection against hybrid genotypes, as well as positive selection in the form of genotypes introgressed into a foreign genetic background. We also found many positive associations of conspecific alleles among unlinked markers, which could be caused by epistatic interactions. Different patterns of introgression in the two transects highlight the challenge of using hybrid zones to identify genes underlying isolation and raise the possibility that the genetic basis of isolation between these species may be dependent on the local population genetic make-up or the local ecological setting.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

Hybridization properties of long nucleic acid probes for detection of variable target sequences, and development of a hybridization prediction algorithm

PubMed Central

Öhrmalm, Christina; Jobs, Magnus; Eriksson, Ronnie; Golbob, Sultan; Elfaitouri, Amal; Benachenhou, Farid; Strømme, Maria; Blomberg, Jonas

2010-01-01

One of the main problems in nucleic acid-based techniques for detection of infectious agents, such as influenza viruses, is that of nucleic acid sequence variation. DNA probes, 70-nt long, some including the nucleotide analog deoxyribose-Inosine (dInosine), were analyzed for hybridization tolerance to different amounts and distributions of mismatching bases, e.g. synonymous mutations, in target DNA. Microsphere-linked 70-mer probes were hybridized in 3M TMAC buffer to biotinylated single-stranded (ss) DNA for subsequent analysis in a Luminex® system. When mismatches interrupted contiguous matching stretches of 6 nt or longer, it had a strong impact on hybridization. Contiguous matching stretches are more important than the same number of matching nucleotides separated by mismatches into several regions. dInosine, but not 5-nitroindole, substitutions at mismatching positions stabilized hybridization remarkably well, comparable to N (4-fold) wobbles in the same positions. In contrast to shorter probes, 70-nt probes with judiciously placed dInosine substitutions and/or wobble positions were remarkably mismatch tolerant, with preserved specificity. An algorithm, NucZip, was constructed to model the nucleation and zipping phases of hybridization, integrating both local and distant binding contributions. It predicted hybridization more exactly than previous algorithms, and has the potential to guide the design of variation-tolerant yet specific probes. PMID:20864443

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

HYBRIDCHECK: software for the rapid detection, visualization and dating of recombinant regions in genome sequence data.

PubMed

Ward, Ben J; van Oosterhout, Cock

2016-03-01

HYBRIDCHECK is a software package to visualize the recombination signal in large DNA sequence data set, and it can be used to analyse recombination, genetic introgression, hybridization and horizontal gene transfer. It can scan large (multiple kb) contigs and whole-genome sequences of three or more individuals. HYBRIDCHECK is written in the r software for OS X, Linux and Windows operating systems, and it has a simple graphical user interface. In addition, the r code can be readily incorporated in scripts and analysis pipelines. HYBRIDCHECK implements several ABBA-BABA tests and visualizes the effects of hybridization and the resulting mosaic-like genome structure in high-density graphics. The package also reports the following: (i) the breakpoint positions, (ii) the number of mutations in each introgressed block, (iii) the probability that the identified region is not caused by recombination and (iv) the estimated age of each recombination event. The divergence times between the donor and recombinant sequence are calculated using a JC, K80, F81, HKY or GTR correction, and the dating algorithm is exceedingly fast. By estimating the coalescence time of introgressed blocks, it is possible to distinguish between hybridization and incomplete lineage sorting. HYBRIDCHECK is libré software and it and its manual are free to download from http://ward9250.github.io/HybridCheck/. © 2015 John Wiley & Sons Ltd.

Two closely related species differ in their regional genetic differentiation despite admixing

PubMed Central

Fischer, Markus; Oja, Tatjana

2018-01-01

Abstract Regional genetic differentiation within species is often addressed in evolutionary ecology and conservation biology. Here, we address regional differentiation in two closely related hybridizing taxa, the perennial sedges Carex flava and C. viridula and their hybrid C. × subviridula in 37 populations in the north and centre of their distribution range in Europe (Estonia, Lowland (<1000 m a.s.l.) and Highland Switzerland) using 10 putative microsatellite loci. We ask whether regional differentiation was larger in the less common taxon C. viridula or whether, possibly due to hybridization, it was similar between taxa. Our results showed similar, low to moderate genetic diversity for the three studied taxa. In total, we found 12 regional species-specific alleles. Analysis of molecular variance (AMOVA), STRUCTURE and multidimensional scaling analysis showed regional structure in genetic variation, where intraspecific differentiation between regions was lower for C. flava (AMOVA: 6.84 %) than for C. viridula (20.77 %) or C. × subviridula (18.27 %) populations. Hybrids differed from the parental taxa in the two regions where they occurred, i.e. in Estonia and Lowland Switzerland. We conclude that C. flava and C. viridula clearly differ from each other genetically, that there is pronounced regional differentiation and that, despite hybridization, this regional differentiation is more pronounced in the less common taxon, C. viridula. We encourage future studies on hybridizing taxa to work with plant populations from more than one region. PMID:29479408

Genetic complexity underlying hybrid male sterility in Drosophila.

PubMed

Sawamura, Kyoichi; Roote, John; Wu, Chung-I; Yamamoto, Masa-Toshi

2004-02-01

Recent genetic analyses of closely related species of Drosophila have indicated that hybrid male sterility is the consequence of highly complex synergistic effects among multiple genes, both conspecific and heterospecific. On the contrary, much evidence suggests the presence of major genes causing hybrid female sterility and inviability in the less-related species, D. melanogaster and D. simulans. Does this contrast reflect the genetic distance between species? Or, generally, is the genetic basis of hybrid male sterility more complex than that of hybrid female sterility and inviability? To clarify this point, the D. simulans introgression of the cytological region 34D-36A to the D. melanogaster genome, which causes recessive male sterility, was dissected by recombination, deficiency, and complementation mapping. The 450-kb region between two genes, Suppressor of Hairless and snail, exhibited a strong effect on the sterility. Males are (semi-)sterile if this region of the introgression is made homozygous or hemizygous. But no genes in the region singly cause the sterility; this region has at least two genes, which in combination result in male sterility. Further, the males are less fertile when heterozygous with a larger introgression, which suggests that dominant modifiers enhance the effects of recessive genes of male sterility. Such an epistatic view, even in the less-related species, suggests that the genetic complexity is special to hybrid male sterility.

Genetic complexity underlying hybrid male sterility in Drosophila.

PubMed Central

Sawamura, Kyoichi; Roote, John; Wu, Chung-I; Yamamoto, Masa-Toshi

2004-01-01

Recent genetic analyses of closely related species of Drosophila have indicated that hybrid male sterility is the consequence of highly complex synergistic effects among multiple genes, both conspecific and heterospecific. On the contrary, much evidence suggests the presence of major genes causing hybrid female sterility and inviability in the less-related species, D. melanogaster and D. simulans. Does this contrast reflect the genetic distance between species? Or, generally, is the genetic basis of hybrid male sterility more complex than that of hybrid female sterility and inviability? To clarify this point, the D. simulans introgression of the cytological region 34D-36A to the D. melanogaster genome, which causes recessive male sterility, was dissected by recombination, deficiency, and complementation mapping. The 450-kb region between two genes, Suppressor of Hairless and snail, exhibited a strong effect on the sterility. Males are (semi-)sterile if this region of the introgression is made homozygous or hemizygous. But no genes in the region singly cause the sterility; this region has at least two genes, which in combination result in male sterility. Further, the males are less fertile when heterozygous with a larger introgression, which suggests that dominant modifiers enhance the effects of recessive genes of male sterility. Such an epistatic view, even in the less-related species, suggests that the genetic complexity is special to hybrid male sterility. PMID:15020468

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Patterns of Reproductive Isolation in Eucalyptus-A Phylogenetic Perspective.

PubMed

Larcombe, Matthew J; Holland, Barbara; Steane, Dorothy A; Jones, Rebecca C; Nicolle, Dean; Vaillancourt, René E; Potts, Brad M

2015-07-01

We assess phylogenetic patterns of hybridization in the speciose, ecologically and economically important genus Eucalyptus, in order to better understand the evolution of reproductive isolation. Eucalyptus globulus pollen was applied to 99 eucalypt species, mainly from the large commercially important subgenus, Symphyomyrtus. In the 64 species that produce seeds, hybrid compatibility was assessed at two stages, hybrid-production (at approximately 1 month) and hybrid-survival (at 9 months), and compared with phylogenies based on 8,350 genome-wide DArT (diversity arrays technology) markers. Model fitting was used to assess the relationship between compatibility and genetic distance, and whether or not the strength of incompatibility "snowballs" with divergence. There was a decline in compatibility with increasing genetic distance between species. Hybridization was common within two closely related clades (one including E. globulus), but rare between E. globulus and species in two phylogenetically distant clades. Of three alternative models tested (linear, slowdown, and snowball), we found consistent support for a snowball model, indicating that the strength of incompatibility accelerates relative to genetic distance. Although we can only speculate about the genetic basis of this pattern, it is consistent with a Dobzhansky-Muller-model prediction that incompatibilities should snowball with divergence due to negative epistasis. Different rates of compatibility decline in the hybrid-production and hybrid-survival measures suggest that early-acting postmating barriers developed first and are stronger than later-acting barriers. We estimated that complete reproductive isolation can take up to 21-31 My in Eucalyptus. Practical implications for hybrid eucalypt breeding and genetic risk assessment in Australia are discussed. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Rapid Genetic and Epigenetic Alterations under Intergeneric Genomic Shock in Newly Synthesized Chrysanthemum morifolium × Leucanthemum paludosum Hybrids (Asteraceae)

PubMed Central

Wang, Haibin; Jiang, Jiafu; Chen, Sumei; Qi, Xiangyu; Fang, Weimin; Guan, Zhiyong; Teng, Nianjun; Liao, Yuan; Chen, Fadi

2014-01-01

The Asteraceae family is at the forefront of the evolution due to frequent hybridization. Hybridization is associated with the induction of widespread genetic and epigenetic changes and has played an important role in the evolution of many plant taxa. We attempted the intergeneric cross Chrysanthemum morifolium × Leucanthemum paludosum. To obtain the success in cross, we have to turn to ovule rescue. DNA profiling of the amphihaploid and amphidiploid was investigated using amplified fragment length polymorphism, sequence-related amplified polymorphism, start codon targeted polymorphism, and methylation-sensitive amplification polymorphism (MSAP). Hybridization induced rapid changes at the genetic and the epigenetic levels. The genetic changes mainly involved loss of parental fragments and gaining of novel fragments, and some eliminated sequences possibly from the noncoding region of L. paludosum. The MSAP analysis indicated that the level of DNA methylation was lower in the amphiploid (∼45%) than in the parental lines (51.5–50.6%), whereas it increased after amphidiploid formation. Events associated with intergeneric genomic shock were a feature of C. morifolium × L. paludosum hybrid, given that the genetic relationship between the parental species is relatively distant. Our results provide genetic and epigenetic evidence for understanding genomic shock in wide crosses between species in Asteraceae and suggest a need to expand our current evolutionary framework to encompass a genetic/epigenetic dimension when seeking to understand wide crosses. PMID:24407856

Genetics-based control of a mimo boiler-turbine plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

A combined ANN-GA and experimental based technique for the estimation of the unknown heat flux for a conjugate heat transfer problem

NASA Astrophysics Data System (ADS)

M K, Harsha Kumar; P S, Vishweshwara; N, Gnanasekaran; C, Balaji

2018-05-01

The major objectives in the design of thermal systems are obtaining the information about thermophysical, transport and boundary properties. The main purpose of this paper is to estimate the unknown heat flux at the surface of a solid body. A constant area mild steel fin is considered and the base is subjected to constant heat flux. During heating, natural convection heat transfer occurs from the fin to ambient. The direct solution, which is the forward problem, is developed as a conjugate heat transfer problem from the fin and the steady state temperature distribution is recorded for any assumed heat flux. In order to model the natural convection heat transfer from the fin, an extended domain is created near the fin geometry and air is specified as a fluid medium and Navier Stokes equation is solved by incorporating the Boussinesq approximation. The computational time involved in executing the forward model is then reduced by developing a neural network (NN) between heat flux values and temperatures based on back propagation algorithm. The conjugate heat transfer NN model is now coupled with Genetic algorithm (GA) for the solution of the inverse problem. Initially, GA is applied to the pure surrogate data, the results are then used as input to the Levenberg- Marquardt method and such hybridization is proven to result in accurate estimation of the unknown heat flux. The hybrid method is then applied for the experimental temperature to estimate the unknown heat flux. A satisfactory agreement between the estimated and actual heat flux is achieved by incorporating the hybrid method.

Application of hybrid artificial fish swarm algorithm based on similar fragments in VRP

NASA Astrophysics Data System (ADS)

Che, Jinnuo; Zhou, Kang; Zhang, Xueyu; Tong, Xin; Hou, Lingyun; Jia, Shiyu; Zhen, Yiting

2018-03-01

Focused on the issue that the decrease of convergence speed and the precision of calculation at the end of the process in Artificial Fish Swarm Algorithm(AFSA) and instability of results, a hybrid AFSA based on similar fragments is proposed. Traditional AFSA enjoys a lot of obvious advantages in solving complex optimization problems like Vehicle Routing Problem(VRP). AFSA have a few limitations such as low convergence speed, low precision and instability of results. In this paper, two improvements are introduced. On the one hand, change the definition of the distance for artificial fish, as well as increase vision field of artificial fish, and the problem of speed and precision can be improved when solving VRP. On the other hand, mix artificial bee colony algorithm(ABC) into AFSA - initialize the population of artificial fish by the ABC, and it solves the problem of instability of results in some extend. The experiment results demonstrate that the optimal solution of the hybrid AFSA is easier to approach the optimal solution of the standard database than the other two algorithms. In conclusion, the hybrid algorithm can effectively solve the problem that instability of results and decrease of convergence speed and the precision of calculation at the end of the process.

Control algorithms for aerobraking in the Martian atmosphere

NASA Technical Reports Server (NTRS)

Ward, Donald T.; Shipley, Buford W., Jr.

1991-01-01

The Analytic Predictor Corrector (APC) and Energy Controller (EC) atmospheric guidance concepts were adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. Changes are made to the APC to improve its robustness to density variations. These changes include adaptation of a new exit phase algorithm, an adaptive transition velocity to initiate the exit phase, refinement of the reference dynamic pressure calculation and two improved density estimation techniques. The modified controller with the hybrid density estimation technique is called the Mars Hybrid Predictor Corrector (MHPC), while the modified controller with a polynomial density estimator is called the Mars Predictor Corrector (MPC). A Lyapunov Steepest Descent Controller (LSDC) is adapted to control the vehicle. The LSDC lacked robustness, so a Lyapunov tracking exit phase algorithm is developed to guide the vehicle along a reference trajectory. This algorithm, when using the hybrid density estimation technique to define the reference path, is called the Lyapunov Hybrid Tracking Controller (LHTC). With the polynomial density estimator used to define the reference trajectory, the algorithm is called the Lyapunov Tracking Controller (LTC). These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. The MHPC, MPC, LHTC, and LTC show dramatic improvements in robustness over the APC and EC.

A hybrid algorithm for clustering of time series data based on affinity search technique.

PubMed

Aghabozorgi, Saeed; Ying Wah, Teh; Herawan, Tutut; Jalab, Hamid A; Shaygan, Mohammad Amin; Jalali, Alireza

2014-01-01

Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.

Decentralized Feedback Controllers for Exponential Stabilization of Hybrid Periodic Orbits: Application to Robotic Walking.

PubMed

Hamed, Kaveh Akbari; Gregg, Robert D

2016-07-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially stabilize periodic orbits for a class of hybrid dynamical systems arising from bipedal walking. The algorithm assumes a class of parameterized and nonlinear decentralized feedback controllers which coordinate lower-dimensional hybrid subsystems based on a common phasing variable. The exponential stabilization problem is translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities, which can be easily solved with available software packages. A set of sufficient conditions for the convergence of the iterative algorithm to a stabilizing decentralized feedback control solution is presented. The power of the algorithm is demonstrated by designing a set of local nonlinear controllers that cooperatively produce stable walking for a 3D autonomous biped with 9 degrees of freedom, 3 degrees of underactuation, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg.

Decentralized Feedback Controllers for Robust Stabilization of Periodic Orbits of Hybrid Systems: Application to Bipedal Walking.

PubMed

Hamed, Kaveh Akbari; Gregg, Robert D

2017-07-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially and robustly stabilize periodic orbits for hybrid dynamical systems against possible uncertainties in discrete-time phases. The algorithm assumes a family of parameterized and decentralized nonlinear controllers to coordinate interconnected hybrid subsystems based on a common phasing variable. The exponential and [Formula: see text] robust stabilization problems of periodic orbits are translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities. By investigating the properties of the Poincaré map, some sufficient conditions for the convergence of the iterative algorithm are presented. The power of the algorithm is finally demonstrated through designing a set of robust stabilizing local nonlinear controllers for walking of an underactuated 3D autonomous bipedal robot with 9 degrees of freedom, impact model uncertainties, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg.

Decentralized Feedback Controllers for Exponential Stabilization of Hybrid Periodic Orbits: Application to Robotic Walking*

PubMed Central

Hamed, Kaveh Akbari; Gregg, Robert D.

2016-01-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially stabilize periodic orbits for a class of hybrid dynamical systems arising from bipedal walking. The algorithm assumes a class of parameterized and nonlinear decentralized feedback controllers which coordinate lower-dimensional hybrid subsystems based on a common phasing variable. The exponential stabilization problem is translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities, which can be easily solved with available software packages. A set of sufficient conditions for the convergence of the iterative algorithm to a stabilizing decentralized feedback control solution is presented. The power of the algorithm is demonstrated by designing a set of local nonlinear controllers that cooperatively produce stable walking for a 3D autonomous biped with 9 degrees of freedom, 3 degrees of underactuation, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. PMID:27990059

Decentralized Feedback Controllers for Robust Stabilization of Periodic Orbits of Hybrid Systems: Application to Bipedal Walking

PubMed Central

Hamed, Kaveh Akbari; Gregg, Robert D.

2016-01-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially and robustly stabilize periodic orbits for hybrid dynamical systems against possible uncertainties in discrete-time phases. The algorithm assumes a family of parameterized and decentralized nonlinear controllers to coordinate interconnected hybrid subsystems based on a common phasing variable. The exponential and H2 robust stabilization problems of periodic orbits are translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities. By investigating the properties of the Poincaré map, some sufficient conditions for the convergence of the iterative algorithm are presented. The power of the algorithm is finally demonstrated through designing a set of robust stabilizing local nonlinear controllers for walking of an underactuated 3D autonomous bipedal robot with 9 degrees of freedom, impact model uncertainties, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. PMID:28959117

A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

PubMed Central

Aghabozorgi, Saeed; Ying Wah, Teh; Herawan, Tutut; Jalab, Hamid A.; Shaygan, Mohammad Amin; Jalali, Alireza

2014-01-01

Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets. PMID:24982966

Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

NASA Astrophysics Data System (ADS)

Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

2013-03-01

In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence is 93.02%, whereas units without landslide occurrence are predicted with an accuracy of 81.13%. To sum up, the verification shows satisfactory agreement with an accuracy of 86.46% between the susceptibility map and the landslide locations. In the landslide susceptibility assessment, ten new slopes were predicted to show potential for failure, which can be confirmed by the engineering geological conditions of these slopes. It was also observed that some disadvantages could be overcome in the application of the neural networks with back propagation, for example, the low convergence rate and local minimum, after the network was optimized using genetic algorithms. To conclude, neural networks with back propagation that are optimized by genetic algorithms are an effective method to predict landslide susceptibility with high accuracy.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

PubMed

Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

2017-07-19

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

Hybrid massively parallel fast sweeping method for static Hamilton-Jacobi equations

NASA Astrophysics Data System (ADS)

Detrixhe, Miles; Gibou, Frédéric

2016-10-01

The fast sweeping method is a popular algorithm for solving a variety of static Hamilton-Jacobi equations. Fast sweeping algorithms for parallel computing have been developed, but are severely limited. In this work, we present a multilevel, hybrid parallel algorithm that combines the desirable traits of two distinct parallel methods. The fine and coarse grained components of the algorithm take advantage of heterogeneous computer architecture common in high performance computing facilities. We present the algorithm and demonstrate its effectiveness on a set of example problems including optimal control, dynamic games, and seismic wave propagation. We give results for convergence, parallel scaling, and show state-of-the-art speedup values for the fast sweeping method.

Overview of existing algorithms for emotion classification. Uncertainties in evaluations of accuracies.

NASA Astrophysics Data System (ADS)

Avetisyan, H.; Bruna, O.; Holub, J.

2016-11-01

A numerous techniques and algorithms are dedicated to extract emotions from input data. In our investigation it was stated that emotion-detection approaches can be classified into 3 following types: Keyword based / lexical-based, learning based, and hybrid. The most commonly used techniques, such as keyword-spotting method, Support Vector Machines, Naïve Bayes Classifier, Hidden Markov Model and hybrid algorithms, have impressive results in this sphere and can reach more than 90% determining accuracy.

Genome-Wide Prediction of the Performance of Three-Way Hybrids in Barley.

PubMed

Li, Zuo; Philipp, Norman; Spiller, Monika; Stiewe, Gunther; Reif, Jochen C; Zhao, Yusheng

2017-03-01

Predicting the grain yield performance of three-way hybrids is challenging. Three-way crosses are relevant for hybrid breeding in barley ( L.) and maize ( L.) adapted to East Africa. The main goal of our study was to implement and evaluate genome-wide prediction approaches of the performance of three-way hybrids using data of single-cross hybrids for a scenario in which parental lines of the three-way hybrids originate from three genetically distinct subpopulations. We extended the ridge regression best linear unbiased prediction (RRBLUP) and devised a genomic selection model allowing for subpopulation-specific marker effects (GSA-RRBLUP: general and subpopulation-specific additive RRBLUP). Using an empirical barley data set, we showed that applying GSA-RRBLUP tripled the prediction ability of three-way hybrids from 0.095 to 0.308 compared with RRBLUP, modeling one additive effect for all three subpopulations. The experimental findings were further substantiated with computer simulations. Our results emphasize the potential of GSA-RRBLUP to improve genome-wide hybrid prediction of three-way hybrids for scenarios of genetically diverse parental populations. Because of the advantages of the GSA-RRBLUP model in dealing with hybrids from different parental populations, it may also be a promising approach to boost the prediction ability for hybrid breeding programs based on genetically diverse heterotic groups. Copyright © 2017 Crop Science Society of America.

Biomedical image segmentation using geometric deformable models and metaheuristics.

PubMed

Mesejo, Pablo; Valsecchi, Andrea; Marrakchi-Kacem, Linda; Cagnoni, Stefano; Damas, Sergio

2015-07-01

This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Thermodynamically accessible titanium clusters TiN, N = 2-32.

PubMed

Lazauskas, Tomas; Sokol, Alexey A; Buckeridge, John; Catlow, C Richard A; Escher, Susanne G E T; Farrow, Matthew R; Mora-Fonz, David; Blum, Volker W; Phaahla, Tshegofatso M; Chauke, Hasani R; Ngoepe, Phuti E; Woodley, Scott M

2018-05-10

We have performed a genetic algorithm search on the tight-binding interatomic potential energy surface (PES) for small TiN (N = 2-32) clusters. The low energy candidate clusters were further refined using density functional theory (DFT) calculations with the PBEsol exchange-correlation functional and evaluated with the PBEsol0 hybrid functional. The resulting clusters were analysed in terms of their structural features, growth mechanism and surface area. The results suggest a growth mechanism that is based on forming coordination centres by interpenetrating icosahedra, icositetrahedra and Frank-Kasper polyhedra. We identify centres of coordination, which act as centres of bulk nucleation in medium sized clusters and determine the morphological features of the cluster.

Learning and Tuning of Fuzzy Rules

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1997-01-01

In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

Evidence of Natural Selection Acting on a Polymorphic Hybrid Incompatibility Locus in Mimulus

PubMed Central

Sweigart, Andrea L.; Flagel, Lex E.

2015-01-01

As a common cause of reproductive isolation in diverse taxa, hybrid incompatibilities are fundamentally important to speciation. A key question is which evolutionary forces drive the initial substitutions within species that lead to hybrid dysfunction. Previously, we discovered a simple genetic incompatibility that causes nearly complete male sterility and partial female sterility in hybrids between the two closely related yellow monkeyflower species Mimulus guttatus and M. nasutus. In this report, we fine map the two major incompatibility loci—hybrid male sterility 1 (hms1) and hybrid male sterility 2 (hms2)—to small nuclear genomic regions (each <70 kb) that include strong candidate genes. With this improved genetic resolution, we also investigate the evolutionary dynamics of hms1 in a natural population of M. guttatus known to be polymorphic at this locus. Using classical genetic crosses and population genomics, we show that a 320-kb region containing the hms1 incompatibility allele has risen to intermediate frequency in this population by strong natural selection. This finding provides direct evidence that natural selection within plant species can lead to hybrid dysfunction between species. PMID:25428983

Facing the phase problem in Coherent Diffractive Imaging via Memetic Algorithms.

PubMed

Colombo, Alessandro; Galli, Davide Emilio; De Caro, Liberato; Scattarella, Francesco; Carlino, Elvio

2017-02-09

Coherent Diffractive Imaging is a lensless technique that allows imaging of matter at a spatial resolution not limited by lens aberrations. This technique exploits the measured diffraction pattern of a coherent beam scattered by periodic and non-periodic objects to retrieve spatial information. The diffracted intensity, for weak-scattering objects, is proportional to the modulus of the Fourier Transform of the object scattering function. Any phase information, needed to retrieve its scattering function, has to be retrieved by means of suitable algorithms. Here we present a new approach, based on a memetic algorithm, i.e. a hybrid genetic algorithm, to face the phase problem, which exploits the synergy of deterministic and stochastic optimization methods. The new approach has been tested on simulated data and applied to the phasing of transmission electron microscopy coherent electron diffraction data of a SrTiO 3 sample. We have been able to quantitatively retrieve the projected atomic potential, and also image the oxygen columns, which are not directly visible in the relevant high-resolution transmission electron microscopy images. Our approach proves to be a new powerful tool for the study of matter at atomic resolution and opens new perspectives in those applications in which effective phase retrieval is necessary.

Age and intraspecific diversity of resilient Acropora communities in Belize

NASA Astrophysics Data System (ADS)

Irwin, Adele; Greer, Lisa; Humston, Robert; Devlin-Durante, Meghann; Cabe, Paul; Lescinsky, Halard; Wirth, Karl; Allen Curran, H.; Baums, Iliana B.

2017-12-01

The corals Acropora palmata and A. cervicornis are important Caribbean reef-builders that have faced significant mortality in recent decades. While many studies have focused on the recent demise of these species, data from areas where Acropora spp. have continued to thrive are limited. Understanding the genetic diversity, recruitment, and temporal continuity of healthy populations of these threatened Acropora spp. and the hybrid they form (" Acropora prolifera") may provide insights into the demographic processes governing them. We studied three reef sites with abundant A. cervicornis, A. palmata, and hybrid Acropora populations offshore of Ambergris Caye, Belize at Coral Gardens, Manatee Channel, and Rocky Point. Samples were collected from all three Acropora taxa. We used microsatellite markers to determine: (1) genotypic diversity; (2) dominant reproductive mode supporting local recruitment; (3) minimum and maximum genet age estimates for all three acroporids; and (4) the history of hybrid colonization at these sites. We found that Acropora populations were highly clonal with local recruitment primarily occurring through asexual fragmentation. We also estimated the ages of 10 Acropora genets using recent methodology based on somatic mutation rates from genetic data. Results indicate minimum ages of 62-409 yr for A. cervicornis, 187-561 yr for A. palmata, and 156-281 yr for the Acropora hybrids at these sites. Our data indicate that existing A. cervicornis, A. palmata, and Acropora hybrid genets persisted during the 1980s Caribbean-wide Acropora spp. collapse, suggesting that these sites have been a refuge for Caribbean Acropora corals. Additionally, our data suggest that formation of extant hybrid Acropora genets pre-dates the widespread collapse of the parent taxa.

Applications of Multiple Nuclear Genes to the Molecular Phylogeny, Population Genetics and Hybrid Identification in the Mangrove Genus Rhizophora.

PubMed

Chen, Yongmei; Hou, Yansong; Guo, Zixiao; Wang, Wenqing; Zhong, Cairong; Zhou, Renchao; Shi, Suhua

2015-01-01

The genus Rhizophora is one of the most important components of mangrove forests. It is an ideal system for studying biogeography, molecular evolution, population genetics, hybridization and conservation genetics of mangroves. However, there are no sufficient molecular markers to address these topics. Here, we developed 77 pairs of nuclear gene primers, which showed successful PCR amplifications across all five Rhizophora species and sequencing in R. apiculata. Here, we present three tentative applications using a subset of the developed nuclear genes to (I) reconstruct the phylogeny, (II) examine the genetic structure and (III) identify natural hybridization in Rhizophora. Phylogenetic analyses support the hypothesis that Rhizophora had disappeared in the Atlantic-East Pacific (AEP) region and was re-colonized from the IWP region approximately 12.7 Mya. Population genetics analyses in four natural populations of R. apiculata in Hainan, China, revealed extremely low genetic diversity, strong population differentiation and extensive admixture, suggesting that the Pleistocene glaciations, particularly the last glacial maximum, greatly influenced the population dynamics of R. apiculata in Hainan. We also verified the hybrid status of a morphologically intermediate individual between R. apiculata and R. stylosa in Hainan. Based on the sequences of five nuclear genes and one chloroplast intergenic spacer, this individual is likely to be an F1 hybrid, with R. stylosa as its maternal parent. The nuclear gene markers developed in this study should be of great value for characterizing the hybridization and introgression patterns in other cases of this genus and testing the role of natural selection using population genomics approaches.

Improving Environmental Model Calibration and Prediction

DTIC Science & Technology

2011-01-18

REPORT Final Report - Improving Environmental Model Calibration and Prediction 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: First, we have continued to...develop tools for efficient global optimization of environmental models. Our algorithms are hybrid algorithms that combine evolutionary strategies...toward practical hybrid optimization tools for environmental models. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 18-01-2011 13

Detecting hybridization between Iranian wild wolf (Canis lupus pallipes) and free-ranging domestic dog (Canis familiaris) by analysis of microsatellite markers.

PubMed

Khosravi, Rasoul; Rezaei, Hamid Reza; Kaboli, Mohammad

2013-01-01

The genetic threat due to hybridization with free-ranging dogs is one major concern in wolf conservation. The identification of hybrids and extent of hybridization is important in the conservation and management of wolf populations. Genetic variation was analyzed at 15 unlinked loci in 28 dogs, 28 wolves, four known hybrids, two black wolves, and one dog with abnormal traits in Iran. Pritchard's model, multivariate ordination by principal component analysis and neighbor joining clustering were used for population clustering and individual assignment. Analysis of genetic variation showed that genetic variability is high in both wolf and dog populations in Iran. Values of H(E) in dog and wolf samples ranged from 0.75-0.92 and 0.77-0.92, respectively. The results of AMOVA showed that the two groups of dog and wolf were significantly different (F(ST) = 0.05 and R(ST) = 0.36; P < 0.001). In each of the three methods, wolf and dog samples were separated into two distinct clusters. Two dark wolves were assigned to the wolf cluster. Also these models detected D32 (dog with abnormal traits) and some other samples, which were assigned to more than one cluster and could be a hybrid. This study is the beginning of a genetic study in wolf populations in Iran, and our results reveal that as in other countries, hybridization between wolves and dogs is sporadic in Iran and can be a threat to wolf populations if human perturbations increase.

Comparison of genetic algorithm methods for fuel management optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-12-31

The CIGARO system was developed for genetic algorithm fuel management optimization. Tests are performed to find the best fuel location swap mutation operator probability and to compare genetic algorithm to a truly random search method. Tests showed the fuel swap probability should be between 0% and 10%, and a 50% definitely hampered the optimization. The genetic algorithm performed significantly better than the random search method, which did not even satisfy the peak normalized power constraint.

Training product unit neural networks with genetic algorithms

NASA Technical Reports Server (NTRS)

Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

1991-01-01

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

New Results in Astrodynamics Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.

1998-01-01

Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.

Computing border bases using mutant strategies

NASA Astrophysics Data System (ADS)

Ullah, E.; Abbas Khan, S.

2014-01-01

Border bases, a generalization of Gröbner bases, have actively been addressed during recent years due to their applicability to industrial problems. In cryptography and coding theory a useful application of border based is to solve zero-dimensional systems of polynomial equations over finite fields, which motivates us for developing optimizations of the algorithms that compute border bases. In 2006, Kehrein and Kreuzer formulated the Border Basis Algorithm (BBA), an algorithm which allows the computation of border bases that relate to a degree compatible term ordering. In 2007, J. Ding et al. introduced mutant strategies bases on finding special lower degree polynomials in the ideal. The mutant strategies aim to distinguish special lower degree polynomials (mutants) from the other polynomials and give them priority in the process of generating new polynomials in the ideal. In this paper we develop hybrid algorithms that use the ideas of J. Ding et al. involving the concept of mutants to optimize the Border Basis Algorithm for solving systems of polynomial equations over finite fields. In particular, we recall a version of the Border Basis Algorithm which is actually called the Improved Border Basis Algorithm and propose two hybrid algorithms, called MBBA and IMBBA. The new mutants variants provide us space efficiency as well as time efficiency. The efficiency of these newly developed hybrid algorithms is discussed using standard cryptographic examples.

Hybridization Leads to Loss of Genetic Integrity in Shortleaf Pine: Unexpected Consequences of Pine Management and Fire Suppression

Treesearch

Charles G. Tauer; John F. Stewart; Rodney E. Will; Curtis J. Lilly; James M. Guldin; C. Dana Nelson

2012-01-01

Hybridization between shortleaf pine and loblolly pine is causing loss of genetic integrity (the tendency of a population to maintain its genotypes over generations) in shortleaf pine, a species already exhibiting dramatic declines due to land-use changes. Recent findings indicate hybridization has increased in shortleaf pine stands from 3% during the 1950s to 45% for...

Sex ratio meiotic drive as a plausible evolutionary mechanism for hybrid male sterility.

PubMed

Zhang, Linbin; Sun, Tianai; Woldesellassie, Fitsum; Xiao, Hailian; Tao, Yun

2015-03-01

Biological diversity on Earth depends on the multiplication of species or speciation, which is the evolution of reproductive isolation such as hybrid sterility between two new species. An unsolved puzzle is the exact mechanism(s) that causes two genomes to diverge from their common ancestor so that some divergent genes no longer function properly in the hybrids. Here we report genetic analyses of divergent genes controlling male fertility and sex ratio in two very young fruitfly species, Drosophila albomicans and D. nasuta. A majority of the genetic divergence for both traits is mapped to the same regions by quantitative trait loci mappings. With introgressions, six major loci are found to contribute to both traits. This genetic colocalization implicates that genes for hybrid male sterility have evolved primarily for controlling sex ratio. We propose that genetic conflicts over sex ratio may operate as a perpetual dynamo for genome divergence. This particular evolutionary mechanism may largely contribute to the rapid evolution of hybrid male sterility and the disproportionate enrichment of its underlying genes on the X chromosome--two patterns widely observed across animals.

Sex Ratio Meiotic Drive as a Plausible Evolutionary Mechanism for Hybrid Male Sterility

PubMed Central

Zhang, Linbin; Xiao, Hailian; Tao, Yun

2015-01-01

Biological diversity on Earth depends on the multiplication of species or speciation, which is the evolution of reproductive isolation such as hybrid sterility between two new species. An unsolved puzzle is the exact mechanism(s) that causes two genomes to diverge from their common ancestor so that some divergent genes no longer function properly in the hybrids. Here we report genetic analyses of divergent genes controlling male fertility and sex ratio in two very young fruitfly species, Drosophila albomicans and D. nasuta. A majority of the genetic divergence for both traits is mapped to the same regions by quantitative trait loci mappings. With introgressions, six major loci are found to contribute to both traits. This genetic colocalization implicates that genes for hybrid male sterility have evolved primarily for controlling sex ratio. We propose that genetic conflicts over sex ratio may operate as a perpetual dynamo for genome divergence. This particular evolutionary mechanism may largely contribute to the rapid evolution of hybrid male sterility and the disproportionate enrichment of its underlying genes on the X chromosome – two patterns widely observed across animals. PMID:25822261

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

PubMed Central

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-01-01

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084

Control of equipment isolation system using wavelet-based hybrid sliding mode control

NASA Astrophysics Data System (ADS)

Huang, Shieh-Kung; Loh, Chin-Hsiung

2017-04-01

Critical non-structural equipment, including life-saving equipment in hospitals, circuit breakers, computers, high technology instrumentations, etc., is vulnerable to strong earthquakes, and on top of that, the failure of the vibration-sensitive equipment will cause severe economic loss. In order to protect vibration-sensitive equipment or machinery against strong earthquakes, various innovative control algorithms are developed to compensate the internal forces that to be applied. These new or improved control strategies, such as the control algorithms based on optimal control theory and sliding mode control (SMC), are also developed for structures engineering as a key element in smart structure technology. The optimal control theory, one of the most common methodologies in feedback control, finds control forces through achieving a certain optimal criterion by minimizing a cost function. For example, the linear-quadratic regulator (LQR) was the most popular control algorithm over the past three decades, and a number of modifications have been proposed to increase the efficiency of classical LQR algorithm. However, except to the advantage of simplicity and ease of implementation, LQR are susceptible to parameter uncertainty and modeling error due to complex nature of civil structures. Different from LQR control, a robust and easy to be implemented control algorithm, SMC has also been studied. SMC is a nonlinear control methodology that forces the structural system to slide along surfaces or boundaries; hence this control algorithm is naturally robust with respect to parametric uncertainties of a structure. Early attempts at protecting vibration-sensitive equipment were based on the use of existing control algorithms as described above. However, in recent years, researchers have tried to renew the existing control algorithms or developing a new control algorithm to adapt the complex nature of civil structures which include the control of both structures and non-structural components. The aim of this paper is to develop a hybrid control algorithm on the control of both structures and equipments simultaneously to overcome the limitations of classical feedback control through combining the advantage of classic LQR and SMC. To suppress vibrations with the frequency contents of strong earthquakes differing from the natural frequencies of civil structures, the hybrid control algorithms integrated with the wavelet-base vibration control algorithm is developed. The performance of classical, hybrid, and wavelet-based hybrid control algorithms as well as the responses of structure and non-structural components are evaluated and discussed through numerical simulation in this study.

Patterns of hybridization of nonnative cutthroat trout and hatchery rainbow trout with native redband trout in the Boise River, Idaho

USGS Publications Warehouse

Neville, Helen M.; Dunham, Jason B.

2011-01-01

Hybridization is one of the greatest threats to native fishes. Threats from hybridization are particularly important for native trout species as stocking of nonnative trout has been widespread within the ranges of native species, thus increasing the potential for hybridization. While many studies have documented hybridization between native cutthroat trout Oncorhynchus clarkii and nonnative rainbow trout O. mykiss, fewer have focused on this issue in native rainbow trout despite widespread threats from introductions of both nonnative cutthroat trout and hatchery rainbow trout. Here, we describe the current genetic (i.e., hybridization) status of native redband trout O. mykiss gairdneri populations in the upper Boise River, Idaho. Interspecific hybridization was widespread (detected at 14 of the 41 sampled locations), but high levels of hybridization between nonnative cutthroat trout and redband trout were detected in only a few streams. Intraspecific hybridization was considerably more widespread (almost 40% of sampled locations), and several local populations of native redband trout have been almost completely replaced with hatchery coastal rainbow trout O. mykiss irideus; other populations exist as hybrid swarms, some are in the process of being actively invaded, and some are maintaining genetic characteristics of native populations. The persistence of some redband trout populations with high genetic integrity provides some opportunity to conserve native genomes, but our findings also highlight the complex decisions facing managers today. Effective management strategies in this system may include analysis of the specific attributes of each site and population to evaluate the relative risks posed by isolation versus maintaining connectivity, identifying potential sites for control or eradication of nonnative trout, and long-term monitoring of the genetic integrity of remaining redband trout populations to track changes in their status.

An agglomerative hierarchical clustering approach to visualisation in Bayesian clustering problems

PubMed Central

Dawson, Kevin J.; Belkhir, Khalid

2009-01-01

Clustering problems (including the clustering of individuals into outcrossing populations, hybrid generations, full-sib families and selfing lines) have recently received much attention in population genetics. In these clustering problems, the parameter of interest is a partition of the set of sampled individuals, - the sample partition. In a fully Bayesian approach to clustering problems of this type, our knowledge about the sample partition is represented by a probability distribution on the space of possible sample partitions. Since the number of possible partitions grows very rapidly with the sample size, we can not visualise this probability distribution in its entirety, unless the sample is very small. As a solution to this visualisation problem, we recommend using an agglomerative hierarchical clustering algorithm, which we call the exact linkage algorithm. This algorithm is a special case of the maximin clustering algorithm that we introduced previously. The exact linkage algorithm is now implemented in our software package Partition View. The exact linkage algorithm takes the posterior co-assignment probabilities as input, and yields as output a rooted binary tree, - or more generally, a forest of such trees. Each node of this forest defines a set of individuals, and the node height is the posterior co-assignment probability of this set. This provides a useful visual representation of the uncertainty associated with the assignment of individuals to categories. It is also a useful starting point for a more detailed exploration of the posterior distribution in terms of the co-assignment probabilities. PMID:19337306

The acylphosphatase (Acyp) alleles associate with male hybrid sterility in Drosophila.

PubMed

Michalak, Pawel; Ma, Daina

2008-06-15

Hybrid defects are believed to result from genetic incompatibilities between genes that have evolved in separate parental lineages. These genetic dysfunctions on the hybrid genomic background, also known as Dobzhansky-Muller incompatibilities, can be an incipient signature of speciation, and as such - a subject of active research. Here we present evidence that Acyp locus (CG16870) that encodes acylphosphatase, a small enzyme that catalyzes the hydrolysis of acylphosphates and participates in ion transport across biological membranes, is involved in genetic incompatibilities leading to male sterility in hybrids between Drosophila simulans and D. mauritiana. There is a strong association between Acyp alleles (genotype) and the sterility/fertility pattern (phenotype), as well as between the phenotype, the genotype and its transcriptional activity. Allele-specific expression in hybrids heterozygous for Acyp suggests a cis-type regulation of this gene, where an allele from one of the parental species (D. simulans) is consistently overexpressed.

High-resolution mapping reveals hundreds of genetic incompatibilities in hybridizing fish species.

PubMed

Schumer, Molly; Cui, Rongfeng; Powell, Daniel L; Dresner, Rebecca; Rosenthal, Gil G; Andolfatto, Peter

2014-06-04

Hybridization is increasingly being recognized as a common process in both animal and plant species. Negative epistatic interactions between genes from different parental genomes decrease the fitness of hybrids and can limit gene flow between species. However, little is known about the number and genome-wide distribution of genetic incompatibilities separating species. To detect interacting genes, we perform a high-resolution genome scan for linkage disequilibrium between unlinked genomic regions in naturally occurring hybrid populations of swordtail fish. We estimate that hundreds of pairs of genomic regions contribute to reproductive isolation between these species, despite them being recently diverged. Many of these incompatibilities are likely the result of natural or sexual selection on hybrids, since intrinsic isolation is known to be weak. Patterns of genomic divergence at these regions imply that genetic incompatibilities play a significant role in limiting gene flow even in young species.

Genetic differentiation and hybrid identification using microsatellite markers in closely related wild species

PubMed Central

Turchetto, Caroline; Segatto, Ana Lúcia A.; Beduschi, Júlia; Bonatto, Sandro L.; Freitas, Loreta B.

2015-01-01

Identifying the genetic basis of speciation is critical for understanding the evolutionary history of closely related wild species. Recently diverged species facilitate the study of speciation because many genetic and morphological characteristics are still shared by the organisms under study. The Petunia genus grows in South American grasslands and comprises both recently diverged wild species and commercial species. In this work, we analysed two closely related species: Petunia exserta, which has a narrow endemic range and grows exclusively in rocky shelters, and Petunia axillaris, which is widely distributed and comprises three allopatric subspecies. Petunia axillaris ssp. axillaris and P. exserta occur in sympatry, and putative hybrids between them have been identified. Here, we analysed 14 expressed sequence tag-simple sequence repeats (EST-SSRs) in 126 wild individuals and 13 putative morphological hybrids with the goals of identifying differentially encoded alleles to characterize their natural genetic diversity, establishing a genetic profile for each taxon and to verify the presence of hybridization signal. Overall, 143 alleles were identified and all taxa contained private alleles. Four major groups were identified in clustering analyses, which indicated that there are genetic distinctions among the groups. The markers evaluated here will be useful in evolutionary studies involving these species and may help categorize individuals by species, thus enabling the identification of hybrids between both their putative taxa. The individuals with intermediate morphology presented private alleles of their both putative parental species, although they showed a level of genetic mixing that was comparable with some of the individuals with typical P. exserta morphology. The EST-SSR markers scattered throughout the Petunia genome are very efficient tools for characterizing the genetic diversity in wild taxa of this genus and aid in identifying interspecific hybrids based on the presence of private alleles. These properties indicate that these markers will be helpful tools in evolutionary studies. PMID:26187606

An introduced and a native vertebrate hybridize to form a genetic bridge to a second native species

USGS Publications Warehouse

McDonald, D.B.; Parchman, T.L.; Bower, M.R.; Hubert, W.A.; Rahel, F.J.

2008-01-01

The genetic impacts of hybridization between native and introduced species are of considerable conservation concern, while the possibility of reticulate evolution affects our basic understanding of how species arise and shapes how we use genetic data to understand evolutionary diversification. By using mitochondrial NADH dehydrogenase subunit 2 (ND2) sequences and 467 amplified fragment-length polymorphism nuclear DNA markers, we show that the introduced white sucker (Catostomus commersoni) has hybridized with two species native to the Colorado River Basin - the flannelmouth sucker (Catostomus latipinnis) and the bluehead sucker (Catostomus discobolus). Hybrids between the flannelmouth sucker and white sucker have facilitated introgression between the two native species, previously isolated by reproductive barriers, such that individuals exist with contributions from all three genomes. Most hybrids had the mitochondrial haplotype of the introduced white sucker, emphasizing its pivotal role in this three-way hybridization. Our findings highlight how introduced species can threaten the genetic integrity of not only one species but also multiple previously reproductively isolated species. Furthermore, this complex three-way reticulate (as opposed to strictly bifurcating) evolution suggests that seeking examples in other vertebrate systems might be productive. Although the present study involved an introduced species, similar patterns of hybridization could result from natural processes, including stream capture or geological formations (e.g., the Bering land bridge). ?? 2008 by The National Academy of Sciences of the USA.

A Hybrid Approach to Protect Palmprint Templates

PubMed Central

Sun, Dongmei; Xiong, Ke; Qiu, Zhengding

2014-01-01

Biometric template protection is indispensable to protect personal privacy in large-scale deployment of biometric systems. Accuracy, changeability, and security are three critical requirements for template protection algorithms. However, existing template protection algorithms cannot satisfy all these requirements well. In this paper, we propose a hybrid approach that combines random projection and fuzzy vault to improve the performances at these three points. Heterogeneous space is designed for combining random projection and fuzzy vault properly in the hybrid scheme. New chaff point generation method is also proposed to enhance the security of the heterogeneous vault. Theoretical analyses of proposed hybrid approach in terms of accuracy, changeability, and security are given in this paper. Palmprint database based experimental results well support the theoretical analyses and demonstrate the effectiveness of proposed hybrid approach. PMID:24982977

A hybrid approach to protect palmprint templates.

PubMed

Liu, Hailun; Sun, Dongmei; Xiong, Ke; Qiu, Zhengding

2014-01-01

Biometric template protection is indispensable to protect personal privacy in large-scale deployment of biometric systems. Accuracy, changeability, and security are three critical requirements for template protection algorithms. However, existing template protection algorithms cannot satisfy all these requirements well. In this paper, we propose a hybrid approach that combines random projection and fuzzy vault to improve the performances at these three points. Heterogeneous space is designed for combining random projection and fuzzy vault properly in the hybrid scheme. New chaff point generation method is also proposed to enhance the security of the heterogeneous vault. Theoretical analyses of proposed hybrid approach in terms of accuracy, changeability, and security are given in this paper. Palmprint database based experimental results well support the theoretical analyses and demonstrate the effectiveness of proposed hybrid approach.