The Design of Power System Stability Controller Based on the PCH Theory and Improved Genetic Algorithm

NASA Astrophysics Data System (ADS)

Liu, Zhijian; Yin, Donghui; Yan, Jun

2017-05-01

Low frequency oscillation is still frequently happened in the power system and it affects the safety and stability of power system directly. With the continuously expending of the interconnection scale of power grid, the risk of low frequency oscillation becomes more and more noticeable. Firstly, the basic theory of port-controlled Hamilton (PCH) and its application is analyzed. Secondly, based on the PCH theory and the dynamic model of system, from the viewpoint of energy, the nonlinear stability controller of power system is designed. By the improved genetic algorithm, the parameters of the PCH model are optimized. Finally, a simulation model with PCH is built to vary the effectiveness of the method proposed in this paper.

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.

The PX-EM algorithm for fast stable fitting of Henderson's mixed model

PubMed Central

Foulley, Jean-Louis; Van Dyk, David A

2000-01-01

This paper presents procedures for implementing the PX-EM algorithm of Liu, Rubin and Wu to compute REML estimates of variance covariance components in Henderson's linear mixed models. The class of models considered encompasses several correlated random factors having the same vector length e.g., as in random regression models for longitudinal data analysis and in sire-maternal grandsire models for genetic evaluation. Numerical examples are presented to illustrate the procedures. Much better results in terms of convergence characteristics (number of iterations and time required for convergence) are obtained for PX-EM relative to the basic EM algorithm in the random regression. PMID:14736399

Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

PubMed

Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

2015-06-18

Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics.

PubMed

Dutheil, Julien; Gaillard, Sylvain; Bazin, Eric; Glémin, Sylvain; Ranwez, Vincent; Galtier, Nicolas; Belkhir, Khalid

2006-04-04

A large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/output methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications. We present Bio++, a set of Object Oriented libraries written in C++. Available components include classes for data storage and handling (nucleotide/amino-acid/codon sequences, trees, distance matrices, population genetics datasets), various input/output formats, basic sequence manipulation (concatenation, transcription, translation, etc.), phylogenetic analysis (maximum parsimony, markov models, distance methods, likelihood computation and maximization), population genetics/genomics (diversity statistics, neutrality tests, various multi-locus analyses) and various algorithms for numerical calculus. Implementation of methods aims at being both efficient and user-friendly. A special concern was given to the library design to enable easy extension and new methods development. We defined a general hierarchy of classes that allow the developer to implement its own algorithms while remaining compatible with the rest of the libraries. Bio++ source code is distributed free of charge under the CeCILL general public licence from its website http://kimura.univ-montp2.fr/BioPP.

Can genetic algorithms help virus writers reshape their creations and avoid detection?

NASA Astrophysics Data System (ADS)

Abu Doush, Iyad; Al-Saleh, Mohammed I.

2017-11-01

Different attack and defence techniques have been evolved over time as actions and reactions between black-hat and white-hat communities. Encryption, polymorphism, metamorphism and obfuscation are among the techniques used by the attackers to bypass security controls. On the other hand, pattern matching, algorithmic scanning, emulation and heuristic are used by the defence team. The Antivirus (AV) is a vital security control that is used against a variety of threats. The AV mainly scans data against its database of virus signatures. Basically, it claims a virus if a match is found. This paper seeks to find the minimal possible changes that can be made on the virus so that it will appear normal when scanned by the AV. Brute-force search through all possible changes can be a computationally expensive task. Alternatively, this paper tries to apply a Genetic Algorithm in solving such a problem. Our proposed algorithm is tested on seven different malware instances. The results show that in all the tested malware instances only a small change in each instance was good enough to bypass the AV.

Weight optimization of plane truss using genetic algorithm

NASA Astrophysics Data System (ADS)

Neeraja, D.; Kamireddy, Thejesh; Santosh Kumar, Potnuru; Simha Reddy, Vijay

2017-11-01

Optimization of structure on basis of weight has many practical benefits in every engineering field. The efficiency is proportionally related to its weight and hence weight optimization gains prime importance. Considering the field of civil engineering, weight optimized structural elements are economical and easier to transport to the site. In this study, genetic optimization algorithm for weight optimization of steel truss considering its shape, size and topology aspects has been developed in MATLAB. Material strength and Buckling stability have been adopted from IS 800-2007 code of construction steel. The constraints considered in the present study are fabrication, basic nodes, displacements, and compatibility. Genetic programming is a natural selection search technique intended to combine good solutions to a problem from many generations to improve the results. All solutions are generated randomly and represented individually by a binary string with similarities of natural chromosomes, and hence it is termed as genetic programming. The outcome of the study is a MATLAB program, which can optimise a steel truss and display the optimised topology along with element shapes, deflections, and stress results.

Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

PubMed

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

PubMed Central

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

Determining Hypocentral Parameters for Local Earthquakes in 1-D Using a Genetic Algorithm and Two-point ray tracing

NASA Astrophysics Data System (ADS)

Kim, W.; Hahm, I.; Ahn, S. J.; Lim, D. H.

2005-12-01

This paper introduces a powerful method for determining hypocentral parameters for local earthquakes in 1-D using a genetic algorithm (GA) and two-point ray tracing. Using existing algorithms to determine hypocentral parameters is difficult, because these parameters can vary based on initial velocity models. We developed a new method to solve this problem by applying a GA to an existing algorithm, HYPO-71 (Lee and Larh, 1975). The original HYPO-71 algorithm was modified by applying two-point ray tracing and a weighting factor with respect to the takeoff angle at the source to reduce errors from the ray path and hypocenter depth. Artificial data, without error, were generated by computer using two-point ray tracing in a true model, in which velocity structure and hypocentral parameters were known. The accuracy of the calculated results was easily determined by comparing calculated and actual values. We examined the accuracy of this method for several cases by changing the true and modeled layer numbers and thicknesses. The computational results show that this method determines nearly exact hypocentral parameters without depending on initial velocity models. Furthermore, accurate and nearly unique hypocentral parameters were obtained, although the number of modeled layers and thicknesses differed from those in the true model. Therefore, this method can be a useful tool for determining hypocentral parameters in regions where reliable local velocity values are unknown. This method also provides the basic a priori information for 3-D studies. KEY -WORDS: hypocentral parameters, genetic algorithm (GA), two-point ray tracing

First Attempt of Orbit Determination of SLR Satellites and Space Debris Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Deleflie, F.; Coulot, D.; Descosta, R.; Fernier, A.; Richard, P.

2013-08-01

We present an orbit determination method based on genetic algorithms. Contrary to usual estimation methods mainly based on least-squares methods, these algorithms do not require any a priori knowledge of the initial state vector to be estimated. These algorithms can be applied when a new satellite is launched or for uncatalogued objects that appear in images obtained from robotic telescopes such as the TAROT ones. We show in this paper preliminary results obtained from an SLR satellite, for which tracking data acquired by the ILRS network enable to build accurate orbital arcs at a few centimeter level, which can be used as a reference orbit ; in this case, the basic observations are made up of time series of ranges, obtained from various tracking stations. We show as well the results obtained from the observations acquired by the two TAROT telescopes on the Telecom-2D satellite operated by CNES ; in that case, the observations are made up of time series of azimuths and elevations, seen from the two TAROT telescopes. The method is carried out in several steps: (i) an analytical propagation of the equations of motion, (ii) an estimation kernel based on genetic algorithms, which follows the usual steps of such approaches: initialization and evolution of a selected population, so as to determine the best parameters. Each parameter to be estimated, namely each initial keplerian element, has to be searched among an interval that is preliminary chosen. The algorithm is supposed to converge towards an optimum over a reasonable computational time.

Finite element analysis and genetic algorithm optimization design for the actuator placement on a large adaptive structure

NASA Astrophysics Data System (ADS)

Sheng, Lizeng

The dissertation focuses on one of the major research needs in the area of adaptive/intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures---optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ˜ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.

A Genetic Algorithm Method for Direct estimation of paleostress states from heterogeneous fault-slip observations

NASA Astrophysics Data System (ADS)

Srivastava, D. C.

2016-12-01

A Genetic Algorithm Method for Direct estimation of paleostress states from heterogeneous fault-slip observationsDeepak C. Srivastava, Prithvi Thakur and Pravin K. GuptaDepartment of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee 247667, India. Abstract Paleostress estimation from a group of heterogeneous fault-slip observations entails first the classification of the observations into homogeneous fault sets and then a separate inversion of each homogeneous set. This study combines these two issues into a nonlinear inverse problem and proposes a heuristic search method that inverts the heterogeneous fault-slip observations. The method estimates different paleostress states in a group of heterogeneous fault-slip observations and classifies it into homogeneous sets as a byproduct. It uses the genetic algorithm operators, elitism, selection, encoding, crossover and mutation. These processes translate into a guided search that finds successively fitter solutions and operate iteratively until the termination criteria is met and the globally fittest stress tensors are obtained. We explain the basic steps of the algorithm on a working example and demonstrate validity of the method on several synthetic and a natural group of heterogeneous fault-slip observations. The method is independent of any user-defined bias or any entrapment of solution in a local optimum. It succeeds even in the difficult situations where other classification methods are found to fail.

Algorithms, complexity, and the sciences

PubMed Central

Papadimitriou, Christos

2014-01-01

Algorithms, perhaps together with Moore’s law, compose the engine of the information technology revolution, whereas complexity—the antithesis of algorithms—is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal—and therefore less compelling—than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene’s cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution. PMID:25349382

Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity.

PubMed

Bahadori, Behnoosh; Atabati, Morteza

2017-01-01

Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Obtaining the phase in the star test using genetic algorithms

NASA Astrophysics Data System (ADS)

Salazar Romero, Marcos A.; Vazquez-Montiel, Sergio; Cornejo-Rodriguez, Alejandro

2004-10-01

The star test is conceptually perhaps the most basic and simplest of all methods of testing image-forming optical systems, the irradiance distribution at the image of a point source (such as a star) is give for the Point Spread Function, PSF. The PSF is very sensitive to aberrations. One way to quantify the PSF is measuring the irradiance distribution on the image of the source point. On the other hand, if we know the aberrations introduced by the optical systems and utilizing the diffraction theory then we can calculate the PSF. In this work we propose a method in order to find the wavefront aberrations starting from the PSF, transforming the problem of fitting a polynomial of aberrations in a problem of optimization using Genetic Algorithm. Also, we show that this method is immune to the noise introduced in the register or recording of the image. Results of these methods are shown.

Crossover versus mutation: a comparative analysis of the evolutionary strategy of genetic algorithms applied to combinatorial optimization problems.

PubMed

Osaba, E; Carballedo, R; Diaz, F; Onieva, E; de la Iglesia, I; Perallos, A

2014-01-01

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.

Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

PubMed Central

Osaba, E.; Carballedo, R.; Diaz, F.; Onieva, E.; de la Iglesia, I.; Perallos, A.

2014-01-01

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test. PMID:25165731

Genetic Algorithms and Local Search

NASA Technical Reports Server (NTRS)

Whitley, Darrell

1996-01-01

The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

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.

Construction of regulatory networks using expression time-series data of a genotyped population.

PubMed

Yeung, Ka Yee; Dombek, Kenneth M; Lo, Kenneth; Mittler, John E; Zhu, Jun; Schadt, Eric E; Bumgarner, Roger E; Raftery, Adrian E

2011-11-29

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

RNA secondary structure prediction using soft computing.

PubMed

Ray, Shubhra Sankar; Pal, Sankar K

2013-01-01

Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

PubMed

Wu, Xiao-Lin; Sun, Chuanyu; Beissinger, Timothy M; Rosa, Guilherme Jm; Weigel, Kent A; Gatti, Natalia de Leon; Gianola, Daniel

2012-09-25

Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

PubMed Central

2012-01-01

Background Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Results Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Conclusions Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs. PMID:23009363

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-06-12

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

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.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

PubMed

Yuce, Baris; Packianather, Michael S; Mastrocinque, Ernesto; Pham, Duc Truong; Lambiase, Alfredo

2013-11-06

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

Crystal Structure Predictions Using Adaptive Genetic Algorithm and Motif Search methods

NASA Astrophysics Data System (ADS)

Ho, K. M.; Wang, C. Z.; Zhao, X.; Wu, S.; Lyu, X.; Zhu, Z.; Nguyen, M. C.; Umemoto, K.; Wentzcovitch, R. M. M.

2017-12-01

Material informatics is a new initiative which has attracted a lot of attention in recent scientific research. The basic strategy is to construct comprehensive data sets and use machine learning to solve a wide variety of problems in material design and discovery. In pursuit of this goal, a key element is the quality and completeness of the databases used. Recent advance in the development of crystal structure prediction algorithms has made it a complementary and more efficient approach to explore the structure/phase space in materials using computers. In this talk, we discuss the importance of the structural motifs and motif-networks in crystal structure predictions. Correspondingly, powerful methods are developed to improve the sampling of the low-energy structure landscape.

A Theoretical Approach to Norm Ecosystems: Two Adaptive Architectures of Indirect Reciprocity Show Different Paths to the Evolution of Cooperation

NASA Astrophysics Data System (ADS)

Uchida, Satoshi; Yamamoto, Hitoshi; Okada, Isamu; Sasaki, Tatsuya

2018-02-01

Indirect reciprocity is one of the basic mechanisms to sustain mutual cooperation, by which beneficial acts are returned, not by the recipient, but by third parties. This mechanism relies on the ability of individuals to know the past actions of others, and to assess those actions. There are many different systems of assessing others, which can be interpreted as rudimentary social norms (i.e., views on what is “good” or “bad”). In this paper, impacts of different adaptive architectures, i.e., ways for individuals to adapt to environments, on indirect reciprocity are investigated. We examine two representative architectures: one based on replicator dynamics and the other on genetic algorithm. Different from the replicator dynamics, the genetic algorithm requires describing the mixture of all possible norms in the norm space under consideration. Therefore, we also propose an analytic method to study norm ecosystems in which all possible second order social norms potentially exist and compete. The analysis reveals that the different adaptive architectures show different paths to the evolution of cooperation. Especially we find that so called Stern-Judging, one of the best studied norms in the literature, exhibits distinct behaviors in both architectures. On one hand, in the replicator dynamics, Stern-Judging remains alive and gets a majority steadily when the population reaches a cooperative state. On the other hand, in the genetic algorithm, it gets a majority only temporarily and becomes extinct in the end.

Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms.

PubMed

Khoje, Suchitra

2018-02-01

Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit quality namely Mango and Guava, and might be applicable to in-line sorting systems. © 2017 Wiley Periodicals, Inc.

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.

Primary chromatic aberration elimination via optimization work with genetic algorithm

NASA Astrophysics Data System (ADS)

Wu, Bo-Wen; Liu, Tung-Kuan; Fang, Yi-Chin; Chou, Jyh-Horng; Tsai, Hsien-Lin; Chang, En-Hao

2008-09-01

Chromatic Aberration plays a part in modern optical systems, especially in digitalized and smart optical systems. Much effort has been devoted to eliminating specific chromatic aberration in order to match the demand for advanced digitalized optical products. Basically, the elimination of axial chromatic and lateral color aberration of an optical lens and system depends on the selection of optical glass. According to reports from glass companies all over the world, the number of various newly developed optical glasses in the market exceeds three hundred. However, due to the complexity of a practical optical system, optical designers have so far had difficulty in finding the right solution to eliminate small axial and lateral chromatic aberration except by the Damped Least Squares (DLS) method, which is limited in so far as the DLS method has not yet managed to find a better optical system configuration. In the present research, genetic algorithms are used to replace traditional DLS so as to eliminate axial and lateral chromatic, by combining the theories of geometric optics in Tessar type lenses and a technique involving Binary/Real Encoding, Multiple Dynamic Crossover and Random Gene Mutation to find a much better configuration for optical glasses. By implementing the algorithms outlined in this paper, satisfactory results can be achieved in eliminating axial and lateral color aberration.

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Identification of isomers and control of ionization and dissociation processes using dual-mass-spectrometer scheme and genetic algorithm optimization

NASA Astrophysics Data System (ADS)

Chen, Zhou; Tong, Qiu-Nan; Zhang, Cong-Cong; Hu, Zhan

2015-04-01

Identification of acetone and its two isomers, and the control of their ionization and dissociation processes are performed using a dual-mass-spectrometer scheme. The scheme employs two sets of time of flight mass spectrometers to simultaneously acquire the mass spectra of two different molecules under the irradiation of identically shaped femtosecond laser pulses. The optimal laser pulses are found using closed-loop learning method based on a genetic algorithm. Compared with the mass spectra of the two isomers that are obtained with the transform limited pulse, those obtained under the irradiation of the optimal laser pulse show large differences and the various reaction pathways of the two molecules are selectively controlled. The experimental results demonstrate that the scheme is quite effective and useful in studies of two molecules having common mass peaks, which makes a traditional single mass spectrometer unfeasible. Project supported by the National Basic Research Program of China (Grant No. 2013CB922200) and the National Natural Science Foundation of China (Grant No. 11374124).

Locating structures and evolution pathways of reconstructed rutile TiO2(011) using genetic algorithm aided density functional theory calculations.

PubMed

Ding, Pan; Gong, Xue-Qing

2016-05-01

Titanium dioxide (TiO2) is an important metal oxide that has been used in many different applications. TiO2 has also been widely employed as a model system to study basic processes and reactions in surface chemistry and heterogeneous catalysis. In this work, we investigated the (011) surface of rutile TiO2 by focusing on its reconstruction. Density functional theory calculations aided by a genetic algorithm based optimization scheme were performed to extensively sample the potential energy surfaces of reconstructed rutile TiO2 structures that obey (2 × 1) periodicity. A lot of stable surface configurations were located, including the global-minimum configuration that was proposed previously. The wide variety of surface structures determined through the calculations performed in this work provide insight into the relationship between the atomic configuration of a surface and its stability. More importantly, several analytical schemes were proposed and tested to gauge the differences and similarities among various surface structures, aiding the construction of the complete pathway for the reconstruction process.

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.

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.

Information resources at the National Center for Biotechnology Information.

PubMed Central

Woodsmall, R M; Benson, D A

1993-01-01

The National Center for Biotechnology Information (NCBI), part of the National Library of Medicine, was established in 1988 to perform basic research in the field of computational molecular biology as well as build and distribute molecular biology databases. The basic research has led to new algorithms and analysis tools for interpreting genomic data and has been instrumental in the discovery of human disease genes for neurofibromatosis and Kallmann syndrome. The principal database responsibility is the National Institutes of Health (NIH) genetic sequence database, GenBank. NCBI, in collaboration with international partners, builds, distributes, and provides online and CD-ROM access to over 112,000 DNA sequences. Another major program is the integration of multiple sequences databases and related bibliographic information and the development of network-based retrieval systems for Internet access. PMID:8374583

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

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.

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

A hybrid genetic algorithm for resolving closely spaced objects

NASA Technical Reports Server (NTRS)

Abbott, R. J.; Lillo, W. E.; Schulenburg, N.

1995-01-01

A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.

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.

Evolving attractive faces using morphing technology and a genetic algorithm: a new approach to determining ideal facial aesthetics.

PubMed

Wong, Brian J F; Karimi, Koohyar; Devcic, Zlatko; McLaren, Christine E; Chen, Wen-Pin

2008-06-01

The objectives of this study were to: 1) determine if a genetic algorithm in combination with morphing software can be used to evolve more attractive faces; and 2) evaluate whether this approach can be used as a tool to define or identify the attributes of the ideal attractive face. Basic research study incorporating focus group evaluations. Digital images were acquired of 250 female volunteers (18-25 y). Randomly selected images were used to produce a parent generation (P) of 30 synthetic faces using morphing software. Then, a focus group of 17 trained volunteers (18-25 y) scored each face on an attractiveness scale ranging from 1 (unattractive) to 10 (attractive). A genetic algorithm was used to select 30 new pairs from the parent generation, and these were morphed using software to produce a new first generation (F1) of faces. The F1 faces were scored by the focus group, and the process was repeated for a total of four iterations of the algorithm. The algorithm mimics natural selection by using the attractiveness score as the selection pressure; the more attractive faces are more likely to morph. All five generations (P-F4) were then scored by three focus groups: a) surgeons (n = 12), b) cos-metology students (n = 44), and c) undergraduate students (n = 44). Morphometric measurements were made of 33 specific features on each of the 150 synthetic faces, and correlated with attractiveness scores using univariate and multivariate analysis. The average facial attractiveness scores increased with each generation and were 3.66 (+0.60), 4.59 (+/-0.73), 5.50 (+/-0.62), 6.23 (+/-0.31), and 6.39 (+/-0.24) for P and F1-F4 generations, respectively. Histograms of attractiveness score distributions show a significant shift in the skew of each curve toward more attractive faces with each generation. Univariate analysis identified nasal width, eyebrow arch height, and lip thickness as being significantly correlated with attractiveness scores. Multivariate analysis identified a similar collection of morphometric measures. No correlation with more commonly accepted measures such as the length facial thirds or fifths were identified. When images are examined as a montage (by generation), clear distinct trends are identified: oval shaped faces, distinct arched eyebrows, and full lips predominate. Faces evolve to approximate the guidelines suggested by classical canons. F3 and F4 generation faces look profoundly similar. The statistical and qualitative analysis indicates that the algorithm and methodology succeeds in generating successively more attractive faces. The use of genetic algorithms in combination with a morphing software and traditional focus-group derived attractiveness scores can be used to evolve attractive synthetic faces. We have demonstrated that the evolution of attractive faces can be mimicked in software. Genetic algorithms and morphing provide a robust alternative to traditional approaches rooted in comparing attractiveness scores with a series of morphometric measurements in human subjects.

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…

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

NASA Astrophysics Data System (ADS)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

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.

Method for hyperspectral imagery exploitation and pixel spectral unmixing

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang (Inventor)

2003-01-01

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

Basic firefly algorithm for document clustering

NASA Astrophysics Data System (ADS)

Mohammed, Athraa Jasim; Yusof, Yuhanis; Husni, Husniza

2015-12-01

The Document clustering plays significant role in Information Retrieval (IR) where it organizes documents prior to the retrieval process. To date, various clustering algorithms have been proposed and this includes the K-means and Particle Swarm Optimization. Even though these algorithms have been widely applied in many disciplines due to its simplicity, such an approach tends to be trapped in a local minimum during its search for an optimal solution. To address the shortcoming, this paper proposes a Basic Firefly (Basic FA) algorithm to cluster text documents. The algorithm employs the Average Distance to Document Centroid (ADDC) as the objective function of the search. Experiments utilizing the proposed algorithm were conducted on the 20Newsgroups benchmark dataset. Results demonstrate that the Basic FA generates a more robust and compact clusters than the ones produced by K-means and Particle Swarm Optimization (PSO).

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.

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.

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.

Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

NASA Astrophysics Data System (ADS)

Moon, Byung-Young

2005-12-01

The hybrid neural-genetic multi-model parameter estimation algorithm was demonstrated. This method can be applied to structured system identification of electro-hydraulic servo system. This algorithms consist of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. To evaluate the proposed method, electro-hydraulic servo system was designed and manufactured. The experiment was carried out to figure out the hybrid neural-genetic multi-model parameter estimation algorithm. As a result, the dynamic characteristics were obtained such as the parameters(mass, damping coefficient, bulk modulus, spring coefficient), which minimize total square error. The result of this study can be applied to hydraulic systems in industrial fields.

Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications.

PubMed

Karnan, M; Thangavel, K

2007-07-01

The presence of microcalcifications in breast tissue is one of the most incident signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. In this paper, the Genetic Algorithm (GA) is proposed for automatic look at commonly prone area the breast border and nipple position to discover the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is to scan left and right images are subtracted to extract the suspicious region. The proposed system consists of two steps: First, the mammogram images are enhanced using median filter, normalize the image, at the pectoral muscle region is excluding the border of the mammogram and comparing for both left and right images from the binary image. Further GA is applied to magnify the detected border. The figure of merit is calculated to evaluate whether the detected border is exact or not. And the nipple position is identified using GA. The some comparisons method is adopted for detection of suspected area. Second, using the border points and nipple position as the reference the mammogram images are aligned and subtracted to extract the suspicious region. The algorithms are tested on 114 abnormal digitized mammograms from Mammogram Image Analysis Society database.

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.

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.

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.

Aerodynamic Optimization of a Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm

DTIC Science & Technology

2016-12-01

Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street Concord, NH 03301 under contract W911SR...Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street... Genetic Algorithm 5a. CONTRACT NUMBER W199SR-15-2-001 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Justin L Paul 5d. PROJECT

Distinguishing genetics and eugenics on the basis of fairness.

PubMed Central

Ledley, F D

1994-01-01

There is concern that human applications of modern genetic technologies may lead inexorably to eugenic abuse. To prevent such abuse, it is essential to have clear, formal principles as well as algorithms for distinguishing genetics from eugenics. This work identifies essential distinctions between eugenics and genetics in the implied nature of the social contract and the importance ascribed to individual welfare relative to society. Rawls's construction of 'justice as fairness' is used as a model for how a formal systems of ethics can be used to proscribe eugenic practices. Rawls's synthesis can be applied to this problem if it is assumed that in the original condition all individuals are ignorant of their genetic constitution and unwilling to consent to social structures which may constrain their own potential. The principles of fairness applied to genetics requires that genetic interventions be directed at extending individual liberties and be applied to the greatest benefit of individuals with the least advantages. These principles are incompatible with negative eugenics which would further penalize those with genetic disadvantage. These principles limit positive eugenics to those practices which are designed to provide absolute benefit to those individuals with least advantage, are acceptable to its subjects, and further a system of basic equal liberties. This analysis also illustrates how simple deviations from first principles in Rawls's formulation could countenance eugenic applications of genetic technologies. PMID:7996561

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

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

Huang, Xiaobiao; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, X. R.; Wang, X.

2016-03-01

When using the genetic algorithm to solve the problem of too-short-arc (TSA) determination, due to the difference of computing processes between the genetic algorithm and classical method, the methods for outliers editing are no longer applicable. In the genetic algorithm, the robust estimation is acquired by means of using different loss functions in the fitness function, then the outlier problem of TSAs is solved. Compared with the classical method, the application of loss functions in the genetic algorithm is greatly simplified. Through the comparison of results of different loss functions, it is clear that the methods of least median square and least trimmed square can greatly improve the robustness of TSAs, and have a high breakdown point.

Bio-Inspired Genetic Algorithms with Formalized Crossover Operators for Robotic Applications.

PubMed

Zhang, Jie; Kang, Man; Li, Xiaojuan; Liu, Geng-Yang

2017-01-01

Genetic algorithms are widely adopted to solve optimization problems in robotic applications. In such safety-critical systems, it is vitally important to formally prove the correctness when genetic algorithms are applied. This paper focuses on formal modeling of crossover operations that are one of most important operations in genetic algorithms. Specially, we for the first time formalize crossover operations with higher-order logic based on HOL4 that is easy to be deployed with its user-friendly programing environment. With correctness-guaranteed formalized crossover operations, we can safely apply them in robotic applications. We implement our technique to solve a path planning problem using a genetic algorithm with our formalized crossover operations, and the results show the effectiveness of our technique.

Maximum volume cuboids for arbitrarily shaped in-situ rock blocks as determined by discontinuity analysis—A genetic algorithm approach

NASA Astrophysics Data System (ADS)

Ülker, Erkan; Turanboy, Alparslan

2009-07-01

The block stone industry is one of the main commercial use of rock. The economic potential of any block quarry depends on the recovery rate, which is defined as the total volume of useful rough blocks extractable from a fixed rock volume in relation to the total volume of moved material. The natural fracture system, the rock type(s) and the extraction method used directly influence the recovery rate. The major aims of this study are to establish a theoretical framework for optimising the extraction process in marble quarries for a given fracture system, and for predicting the recovery rate of the excavated blocks. We have developed a new approach by taking into consideration only the fracture structure for maximum block recovery in block quarries. The complete model uses a linear approach based on basic geometric features of discontinuities for 3D models, a tree structure (TS) for individual investigation and finally a genetic algorithm (GA) for the obtained cuboid volume(s). We tested our new model in a selected marble quarry in the town of İscehisar (AFYONKARAHİSAR—TURKEY).

Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm

NASA Astrophysics Data System (ADS)

Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi

2012-03-01

This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.

Computational principles underlying recognition of acoustic signals in grasshoppers and crickets.

PubMed

Ronacher, Bernhard; Hennig, R Matthias; Clemens, Jan

2015-01-01

Grasshoppers and crickets independently evolved hearing organs and acoustic communication. They differ considerably in the organization of their auditory pathways, and the complexity of their songs, which are essential for mate attraction. Recent approaches aimed at describing the behavioral preference functions of females in both taxa by a simple modeling framework. The basic structure of the model consists of three processing steps: (1) feature extraction with a bank of 'LN models'-each containing a linear filter followed by a nonlinearity, (2) temporal integration, and (3) linear combination. The specific properties of the filters and nonlinearities were determined using a genetic learning algorithm trained on a large set of different song features and the corresponding behavioral response scores. The model showed an excellent prediction of the behavioral responses to the tested songs. Most remarkably, in both taxa the genetic algorithm found Gabor-like functions as the optimal filter shapes. By slight modifications of Gabor filters several types of preference functions could be modeled, which are observed in different cricket species. Furthermore, this model was able to explain several so far enigmatic results in grasshoppers. The computational approach offered a remarkably simple framework that can account for phenotypically rather different preference functions across several taxa.

Optimization of chiral lattice based metastructures for broadband vibration suppression using genetic algorithms

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

2016-05-01

One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm

NASA Astrophysics Data System (ADS)

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm.

PubMed

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.

[The emphases and basic procedures of genetic counseling in psychotherapeutic model].

PubMed

Zhang, Yuan-Zhi; Zhong, Nanbert

2006-11-01

The emphases and basic procedures of genetic counseling are all different with those in old models. In the psychotherapeutic model, genetic counseling will not only focus on counselees' genetic disorders and birth defects, but also their psychological problems. "Client-centered therapy" termed by Carl Rogers plays an important role in genetic counseling process. The basic procedures of psychotherapeutic model of genetic counseling include 7 steps: initial contact, introduction, agendas, inquiry of family history, presenting information, closing the session and follow-up.

A Test of Genetic Algorithms in Relevance Feedback.

ERIC Educational Resources Information Center

Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de

2002-01-01

Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…

Transonic Wing Shape Optimization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

2002-01-01

A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

Portfolio optimization by using linear programing models based on genetic algorithm

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

An improved genetic algorithm and its application in the TSP problem

NASA Astrophysics Data System (ADS)

Li, Zheng; Qin, Jinlei

2011-12-01

Concept and research actuality of genetic algorithm are introduced in detail in the paper. Under this condition, the simple genetic algorithm and an improved algorithm are described and applied in an example of TSP problem, where the advantage of genetic algorithm is adequately shown in solving the NP-hard problem. In addition, based on partial matching crossover operator, the crossover operator method is improved into extended crossover operator in order to advance the efficiency when solving the TSP. In the extended crossover method, crossover operator can be performed between random positions of two random individuals, which will not be restricted by the position of chromosome. Finally, the nine-city TSP is solved using the improved genetic algorithm with extended crossover method, the efficiency of whose solution process is much higher, besides, the solving speed of the optimal solution is much faster.

Solving TSP problem with improved genetic algorithm

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

2018-05-01

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Evolving Attractive Faces Using Morphing Technology and a Genetic Algorithm: A New Approach to Determining Ideal Facial Aesthetics

PubMed Central

Wong, Brian J. F.; Karmi, Koohyar; Devcic, Zlatko; McLaren, Christine E.; Chen, Wen-Pin

2013-01-01

Objectives The objectives of this study were to: 1) determine if a genetic algorithm in combination with morphing software can be used to evolve more attractive faces; and 2) evaluate whether this approach can be used as a tool to define or identify the attributes of the ideal attractive face. Study Design Basic research study incorporating focus group evaluations. Methods Digital images were acquired of 250 female volunteers (18–25 y). Randomly selected images were used to produce a parent generation (P) of 30 synthetic faces using morphing software. Then, a focus group of 17 trained volunteers (18–25 y) scored each face on an attractiveness scale ranging from 1 (unattractive) to 10 (attractive). A genetic algorithm was used to select 30 new pairs from the parent generation, and these were morphed using software to produce a new first generation (F1) of faces. The F1 faces were scored by the focus group, and the process was repeated for a total of four iterations of the algorithm. The algorithm mimics natural selection by using the attractiveness score as the selection pressure; the more attractive faces are more likely to morph. All five generations (P-F4) were then scored by three focus groups: a) surgeons (n = 12), b) cosmetology students (n = 44), and c) undergraduate students (n = 44). Morphometric measurements were made of 33 specific features on each of the 150 synthetic faces, and correlated with attractiveness scores using univariate and multivariate analysis. Results The average facial attractiveness scores increased with each generation and were 3.66 (+0.60), 4.59 (±0.73), 5.50 (±0.62), 6.23 (±0.31), and 6.39 (±0.24) for P and F1–F4 generations, respectively. Histograms of attractiveness score distributions show a significant shift in the skew of each curve toward more attractive faces with each generation. Univariate analysis identified nasal width, eyebrow arch height, and lip thickness as being significantly correlated with attractiveness scores. Multivariate analysis identified a similar collection of morphometric measures. No correlation with more commonly accepted measures such as the length facial thirds or fifths were identified. When images are examined as a montage (by generation), clear distinct trends are identified: oval shaped faces, distinct arched eyebrows, and full lips predominate. Faces evolve to approximate the guidelines suggested by classical canon. F3 and F4 generation faces look profoundly similar. The statistical and qualitative analysis indicates that the algorithm and methodology succeeds in generating successively more attractive faces. Conclusions The use of genetic algorithms in combination with a morphing software and traditional focus-group derived attractiveness scores can be used to evolve attractive synthetic faces. We have demonstrated that the evolution of attractive faces can be mimicked in software. Genetic algorithms and morphing provide a robust alternative to traditional approaches rooted in comparing attractiveness scores with a series of morphometric measurements in human subjects. PMID:18401273

A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates

ERIC Educational Resources Information Center

Venables, Anne; Tan, Grace

2007-01-01

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…

The potential of genetic algorithms for conceptual design of rotor systems

NASA Technical Reports Server (NTRS)

Crossley, William A.; Wells, Valana L.; Laananen, David H.

1993-01-01

The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, Xin-ran; Wang, Xin

2017-04-01

When the genetic algorithm is used to solve the problem of too short-arc (TSA) orbit determination, due to the difference of computing process between the genetic algorithm and the classical method, the original method for outlier deletion is no longer applicable. In the genetic algorithm, the robust estimation is realized by introducing different loss functions for the fitness function, then the outlier problem of the TSA orbit determination is solved. Compared with the classical method, the genetic algorithm is greatly simplified by introducing in different loss functions. Through the comparison on the calculations of multiple loss functions, it is found that the least median square (LMS) estimation and least trimmed square (LTS) estimation can greatly improve the robustness of the TSA orbit determination, and have a high breakdown point.

An Improved Heuristic Method for Subgraph Isomorphism Problem

NASA Astrophysics Data System (ADS)

Xiang, Yingzhuo; Han, Jiesi; Xu, Haijiang; Guo, Xin

2017-09-01

This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.

Four (Algorithms) in One (Bag): An Integrative Framework of Knowledge for Teaching the Standard Algorithms of the Basic Arithmetic Operations

ERIC Educational Resources Information Center

Raveh, Ira; Koichu, Boris; Peled, Irit; Zaslavsky, Orit

2016-01-01

In this article we present an integrative framework of knowledge for teaching the standard algorithms of the four basic arithmetic operations. The framework is based on a mathematical analysis of the algorithms, a connectionist perspective on teaching mathematics and an analogy with previous frameworks of knowledge for teaching arithmetic…

Genetic algorithms for adaptive real-time control in space systems

NASA Technical Reports Server (NTRS)

Vanderzijp, J.; Choudry, A.

1988-01-01

Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

PubMed

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

PubMed Central

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011

Scalability problems of simple genetic algorithms.

PubMed

Thierens, D

1999-01-01

Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm-namely elitism, niching, and restricted mating are not significantly improving the scalability problems.

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

PubMed

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

Linear genetic programming application for successive-station monthly streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

2014-09-01

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

An investigation of messy genetic algorithms

NASA Technical Reports Server (NTRS)

Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

1990-01-01

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

Global Optimization of a Periodic System using a Genetic Algorithm

NASA Astrophysics Data System (ADS)

Stucke, David; Crespi, Vincent

2001-03-01

We use a novel application of a genetic algorithm global optimizatin technique to find the lowest energy structures for periodic systems. We apply this technique to colloidal crystals for several different stoichiometries of binary and trinary colloidal crystals. This application of a genetic algorithm is decribed and results of likely candidate structures are presented.

Research and application of multi-agent genetic algorithm in tower defense game

NASA Astrophysics Data System (ADS)

Jin, Shaohua

2018-04-01

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.

Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2001-01-01

A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

Learning Genetics with Paper Pets

ERIC Educational Resources Information Center

Finnerty, Valerie Raunig

2006-01-01

By the end of the eighth grade, students are expected to have a basic understanding of the mechanism of basic genetic inheritance. However, these concepts can be difficult to teach. In this article, the author introduces a new learning tool that will help facilitate student learning and enthusiasm to the basic concepts of genetic inheritance. This…

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

USGS Publications Warehouse

Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

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.

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

PubMed Central

Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements. PMID:29209364

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.

PubMed

Hussain, Abid; Muhammad, Yousaf Shad; Nauman Sajid, M; Hussain, Ijaz; Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

NASA Astrophysics Data System (ADS)

Thammano, Arit; Teekeng, Wannaporn

2015-05-01

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

A New Challenge for Compression Algorithms: Genetic Sequences.

ERIC Educational Resources Information Center

Grumbach, Stephane; Tahi, Fariza

1994-01-01

Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…

HyphArea--automated analysis of spatiotemporal fungal patterns.

PubMed

Baum, Tobias; Navarro-Quezada, Aura; Knogge, Wolfgang; Douchkov, Dimitar; Schweizer, Patrick; Seiffert, Udo

2011-01-01

In phytopathology quantitative measurements are rarely used to assess crop plant disease symptoms. Instead, a qualitative valuation by eye is often the method of choice. In order to close the gap between subjective human inspection and objective quantitative results, the development of an automated analysis system that is capable of recognizing and characterizing the growth patterns of fungal hyphae in micrograph images was developed. This system should enable the efficient screening of different host-pathogen combinations (e.g., barley-Blumeria graminis, barley-Rhynchosporium secalis) using different microscopy technologies (e.g., bright field, fluorescence). An image segmentation algorithm was developed for gray-scale image data that achieved good results with several microscope imaging protocols. Furthermore, adaptability towards different host-pathogen systems was obtained by using a classification that is based on a genetic algorithm. The developed software system was named HyphArea, since the quantification of the area covered by a hyphal colony is the basic task and prerequisite for all further morphological and statistical analyses in this context. By means of a typical use case the utilization and basic properties of HyphArea could be demonstrated. It was possible to detect statistically significant differences between the growth of an R. secalis wild-type strain and a virulence mutant. Copyright © 2010 Elsevier GmbH. All rights reserved.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Refined genetic algorithm -- Economic dispatch example

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

Sheble, G.B.; Brittig, K.

1995-02-01

A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.

Immune allied genetic algorithm for Bayesian network structure learning

NASA Astrophysics Data System (ADS)

Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

Flexible Space-Filling Designs for Complex System Simulations

DTIC Science & Technology

2013-06-01

interior of the experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with...Computer Experiments, Design of Experiments, Genetic Algorithm , Latin Hypercube, Response Surface Methodology, Nearly Orthogonal 15. NUMBER OF PAGES 147...experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with minimal correlations

Genetic algorithms in conceptual design of a light-weight, low-noise, tilt-rotor aircraft

NASA Technical Reports Server (NTRS)

Wells, Valana L.

1996-01-01

This report outlines research accomplishments in the area of using genetic algorithms (GA) for the design and optimization of rotorcraft. It discusses the genetic algorithm as a search and optimization tool, outlines a procedure for using the GA in the conceptual design of helicopters, and applies the GA method to the acoustic design of rotors.

Self-calibration of a noisy multiple-sensor system with genetic algorithms

NASA Astrophysics Data System (ADS)

Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua

1996-01-01

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.

Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.

Design quadrilateral apertures in binary computer-generated holograms of large space bandwidth product.

PubMed

Wang, Jing; Sheng, Yunlong

2016-09-20

A new approach for designing the binary computer-generated hologram (CGH) of a very large number of pixels is proposed. Diffraction of the CGH apertures is computed by the analytical Abbe transform and by considering the aperture edges as the basic diffracting elements. The computation cost is independent of the CGH size. The arbitrary-shaped polygonal apertures in the CGH consist of quadrilateral apertures, which are designed by assigning the binary phases using the parallel genetic algorithm with a local search, followed by optimizing the locations of the co-vertices with a direct search. The design results in high performance with low image reconstruction error.

Multidisciplinary Techniques and Novel Aircraft Control Systems

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Rogers, James L.; Raney, David L.

2000-01-01

The Aircraft Morphing Program at NASA Langley Research Center explores opportunities to improve airframe designs with smart technologies. Two elements of this basic research program are multidisciplinary design optimization (MDO) and advanced flow control. This paper describes examples where MDO techniques such as sensitivity analysis, automatic differentiation, and genetic algorithms contribute to the design of novel control systems. In the test case, the design and use of distributed shape-change devices to provide low-rate maneuvering capability for a tailless aircraft is considered. The ability of MDO to add value to control system development is illustrated using results from several years of research funded by the Aircraft Morphing Program.

Multidisciplinary Techniques and Novel Aircraft Control Systems

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Rogers, James L.; Raney, David L.

2000-01-01

The Aircraft Morphing Program at NASA Langley Research Center explores opportunities to improve airframe designs with smart technologies. Two elements of this basic research program are multidisciplinary design optimization (MDO) and advanced flow control. This paper describes examples where MDO techniques such as sensitivity analysis, automatic differentiation, and genetic algorithms contribute to the design of novel control systems. In the test case, the design and use of distributed shapechange devices to provide low-rate maneuvering capability for a tailless aircraft is considered. The ability of MDO to add value to control system development is illustrated using results from several years of research funded by the Aircraft Morphing Program.

Paradigms for machine learning

NASA Technical Reports Server (NTRS)

Schlimmer, Jeffrey C.; Langley, Pat

1991-01-01

Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.

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

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

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.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Pose estimation for augmented reality applications using genetic algorithm.

PubMed

Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

2005-12-01

This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.

Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm

NASA Technical Reports Server (NTRS)

Le Riche, Rodolphe; Haftka, Raphael T.

1992-01-01

The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, Shou-ping; Xin, Xiao-kang

2017-07-01

Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.

Engineered Intrinsic Bioremediation of Ammonium Perchlorate in Groundwater

DTIC Science & Technology

2010-12-01

German Collection of Microorganisms and Cell Cultures) GA Genetic Algorithms GA-ANN Genetic Algorithm Artificial Neural Network GMO genetically...for in situ treatment of perchlorate in groundwater. This is accomplished without the addition of genetically engineered microorganisms ( GMOs ) to the...perchlorate, even in the presence of oxygen and without the addition of genetically engineered microorganisms ( GMOs ) to the environment. This approach

Genetic and Environmental Influences on Behavior: Capturing All the Interplay

ERIC Educational Resources Information Center

Johnson, Wendy

2007-01-01

Basic quantitative genetic models of human behavioral variation have made clear that individual differences in behavior cannot be understood without acknowledging the importance of genetic influences. Yet these basic models estimate average, population-level genetic and environmental influences, obscuring differences that might exist within the…

[Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].

PubMed

Smirnova, N I; Agafonov, D A; Zadnova, S P; Cherkasov, A V; Kutyrev, V V

2014-01-01

Development of an algorithm of genetically altered Vibrio cholerae biovar El Tor strai identification that ensures determination of serogroup, serovar and biovar of the studied isolate based on pheno- and genotypic properties, detection of genetically altered cholera El Tor causative agents, their differentiation by epidemic potential as well as evaluation of variability of key pathogenicity genes. Complex analysis of 28 natural V. cholerae strains was carried out by using traditional microbiological methods, PCR and fragmentary sequencing. An algorithm of toxigenic genetically altered V. cholerae biovar El Tor strain identification was developed that includes 4 stages: determination of serogroup, serovar and biovar based on phenotypic properties, confirmation of serogroup and biovar based on molecular-genetic properties determination of strains as genetically altered, differentiation of genetically altered strains by their epidemic potential and detection of ctxB and tcpA key pathogenicity gene polymorphism. The algorithm is based on the use of traditional microbiological methods, PCR and sequencing of gene fragments. The use of the developed algorithm will increase the effectiveness of detection of genetically altered variants of the cholera El Tor causative agent, their differentiation by epidemic potential and will ensure establishment of polymorphism of genes that code key pathogenicity factors for determination of origins of the strains and possible routes of introduction of the infection.

It's Not Your Grandmother's Genetics Anymore!

ERIC Educational Resources Information Center

Smith, Mike U.

2014-01-01

Genetics is perhaps the most rapidly growing field of science today. Recent findings such as those of the Human Genome Project have led to new understandings of basic genetic phenomena and even to increased confusion about some basic genetic ideas, such as the nature of the gene. These developments directly influence how we should teach genetics.…

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

Distributed genetic algorithms for the floorplan design problem

NASA Technical Reports Server (NTRS)

Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.

1991-01-01

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

Evolving aerodynamic airfoils for wind turbines through a genetic algorithm

NASA Astrophysics Data System (ADS)

Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI

2017-01-01

Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.

Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs.

NASA Astrophysics Data System (ADS)

Hawary, A. F.; Razak, N. A.

2018-05-01

Whilst UAV offers a potentially cheaper and more localized observation platform than current satellite or land-based approaches, it requires an advance path planner to reveal its true potential, particularly in real-time missions. Manual control by human will have limited line-of-sights and prone to errors due to careless and fatigue. A good alternative solution is to equip the UAV with semi-autonomous capabilities that able to navigate via a pre-planned route in real-time fashion. In this paper, we propose an easy-and-practical path optimizer based on the classical Travelling Salesman Problem and adopts a brute force search method to re-optimize the route in the event of collisions using range finder sensor. The former utilizes a Simple Genetic Algorithm and the latter uses Nearest Neighbour algorithm. Both algorithms are combined to optimize the route and avoid collision at once. Although many researchers proposed various path planning algorithms, we find that it is difficult to integrate on a basic UAV model and often lacks of real-time collision detection optimizer. Therefore, we explore a practical benefit from this approach using on-board Arduino and Ardupilot controllers by manually emulating the motion of an actual UAV model prior to test on the flying site. The result showed that the range finder sensor provides a real-time data to the algorithm to find a collision-free path and eventually optimized the route successfully.

An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Weir, John M.; Wells, B. Earl

2003-01-01

Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating

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

Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.

1999-04-12

The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a generalmore » parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.« less

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

NASA Astrophysics Data System (ADS)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

Performance Analysis of Combined Methods of Genetic Algorithm and K-Means Clustering in Determining the Value of Centroid

NASA Astrophysics Data System (ADS)

Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna

2017-12-01

The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Cloud computing-based TagSNP selection algorithm for human genome data.

PubMed

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-05

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

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.

The Applications of Genetic Algorithms in Medicine.

PubMed

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-11-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

The Applications of Genetic Algorithms in Medicine

PubMed Central

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-01-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

PubMed Central

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-01

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. PMID:25569088

Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

A genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2012-01-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

A genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2011-12-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Benford, Andrew; Tinker, Michael L.

2004-01-01

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

Superscattering of light optimized by a genetic algorithm

NASA Astrophysics Data System (ADS)

Mirzaei, Ali; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.

2014-07-01

We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

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

Neural-network-assisted genetic algorithm applied to silicon clusters

NASA Astrophysics Data System (ADS)

Marim, L. R.; Lemes, M. R.; dal Pino, A.

2003-03-01

Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Sin (10⩽n⩽15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.

Contradictory genetic make-up of Dutch harbour porpoises: Response to van der Plas-Duivesteijn et al.

NASA Astrophysics Data System (ADS)

Kopps, Anna M.; Palsbøll, Per J.

2016-02-01

The assessment of the status of endangered species or populations typically draw generously on the plethora of population genetic software available to detect population genetic structuring. However, despite the many available analytical approaches, population genetic inference methods [of neutral genetic variation] essentially capture three basic processes; migration, random genetic drift and mutation. Consequently, different analytical approaches essentially capture the same basic process, and should yield consistent results.

Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

ERIC Educational Resources Information Center

Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

2003-01-01

Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

EPA Science Inventory

Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...

Cloud identification using genetic algorithms and massively parallel computation

NASA Technical Reports Server (NTRS)

Buckles, Bill P.; Petry, Frederick E.

1996-01-01

As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user's manual was written and distributed nationwide to scientists whose work might benefit from its availability. Several papers, including two journal articles, were produced.

A genetic algorithm for solving supply chain network design model

NASA Astrophysics Data System (ADS)

Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

2013-09-01

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

The simulation method of chemical composition of vermicular graphite iron on the basis of genetic algorithm

NASA Astrophysics Data System (ADS)

Yusupov, L. R.; Klochkova, K. V.; Simonova, L. A.

2017-09-01

The paper presents a methodology of modeling the chemical composition of the composite material via genetic algorithm for optimization of the manufacturing process of products. The paper presents algorithms of methods based on intelligent system of vermicular graphite iron design

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

EPA Science Inventory

A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

DTIC Science & Technology

2010-03-01

17 NSGA-II non-dominated sorting genetic algorithm II . . . . . . . . . . . . . . . . . . . 17 jMetal Metaheuristic Algorithms in...to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of...problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

Combinatorial Multiobjective Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Crossley, William A.; Martin. Eric T.

2002-01-01

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

ScaffoldScaffolder: solving contig orientation via bidirected to directed graph reduction.

PubMed

Bodily, Paul M; Fujimoto, M Stanley; Snell, Quinn; Ventura, Dan; Clement, Mark J

2016-01-01

The contig orientation problem, which we formally define as the MAX-DIR problem, has at times been addressed cursorily and at times using various heuristics. In setting forth a linear-time reduction from the MAX-CUT problem to the MAX-DIR problem, we prove the latter is NP-complete. We compare the relative performance of a novel greedy approach with several other heuristic solutions. Our results suggest that our greedy heuristic algorithm not only works well but also outperforms the other algorithms due to the nature of scaffold graphs. Our results also demonstrate a novel method for identifying inverted repeats and inversion variants, both of which contradict the basic single-orientation assumption. Such inversions have previously been noted as being difficult to detect and are directly involved in the genetic mechanisms of several diseases. http://bioresearch.byu.edu/scaffoldscaffolder. paulmbodily@gmail.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun

2009-10-01

To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

A., Javadpour; A., Mohammadi

2016-01-01

Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

NVU dynamics. I. Geodesic motion on the constant-potential-energy hypersurface.

PubMed

Ingebrigtsen, Trond S; Toxvaerd, Søren; Heilmann, Ole J; Schrøder, Thomas B; Dyre, Jeppe C

2011-09-14

An algorithm is derived for computer simulation of geodesics on the constant-potential-energy hypersurface of a system of N classical particles. First, a basic time-reversible geodesic algorithm is derived by discretizing the geodesic stationarity condition and implementing the constant-potential-energy constraint via standard Lagrangian multipliers. The basic NVU algorithm is tested by single-precision computer simulations of the Lennard-Jones liquid. Excellent numerical stability is obtained if the force cutoff is smoothed and the two initial configurations have identical potential energy within machine precision. Nevertheless, just as for NVE algorithms, stabilizers are needed for very long runs in order to compensate for the accumulation of numerical errors that eventually lead to "entropic drift" of the potential energy towards higher values. A modification of the basic NVU algorithm is introduced that ensures potential-energy and step-length conservation; center-of-mass drift is also eliminated. Analytical arguments confirmed by simulations demonstrate that the modified NVU algorithm is absolutely stable. Finally, we present simulations showing that the NVU algorithm and the standard leap-frog NVE algorithm have identical radial distribution functions for the Lennard-Jones liquid. © 2011 American Institute of Physics

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

Review: Optimization methods for groundwater modeling and management

NASA Astrophysics Data System (ADS)

Yeh, William W.-G.

2015-09-01

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

Lo, C C; Chang, W H

2000-01-01

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Genetic Algorithm Approaches for Actuator Placement

NASA Technical Reports Server (NTRS)

Crossley, William A.

2000-01-01

This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.

A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm

NASA Astrophysics Data System (ADS)

Thirer, Nonel

2013-05-01

With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

Image reconstruction through thin scattering media by simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

2018-07-01

An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.

Algorithms for Disconnected Diagrams in Lattice QCD

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

Gambhir, Arjun Singh; Stathopoulos, Andreas; Orginos, Konstantinos

2016-11-01

Computing disconnected diagrams in Lattice QCD (operator insertion in a quark loop) entails the computationally demanding problem of taking the trace of the all to all quark propagator. We first outline the basic algorithm used to compute a quark loop as well as improvements to this method. Then, we motivate and introduce an algorithm based on the synergy between hierarchical probing and singular value deflation. We present results for the chiral condensate using a 2+1-flavor clover ensemble and compare estimates of the nucleon charges with the basic algorithm.

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«.

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

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

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.

Automatic page layout using genetic algorithms for electronic albuming

NASA Astrophysics Data System (ADS)

Geigel, Joe; Loui, Alexander C. P.

2000-12-01

In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.

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.

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing-based visible light communication systems using a modified partial transmit sequence technique

NASA Astrophysics Data System (ADS)

Liu, Yan; Deng, Honggui; Ren, Shuang; Tang, Chengying; Qian, Xuewen

2018-01-01

We propose an efficient partial transmit sequence technique based on genetic algorithm and peak-value optimization algorithm (GAPOA) to reduce high peak-to-average power ratio (PAPR) in visible light communication systems based on orthogonal frequency division multiplexing (VLC-OFDM). By analysis of hill-climbing algorithm's pros and cons, we propose the POA with excellent local search ability to further process the signals whose PAPR is still over the threshold after processed by genetic algorithm (GA). To verify the effectiveness of the proposed technique and algorithm, we evaluate the PAPR performance and the bit error rate (BER) performance and compare them with partial transmit sequence (PTS) technique based on GA (GA-PTS), PTS technique based on genetic and hill-climbing algorithm (GH-PTS), and PTS based on shuffled frog leaping algorithm and hill-climbing algorithm (SFLAHC-PTS). The results show that our technique and algorithm have not only better PAPR performance but also lower computational complexity and BER than GA-PTS, GH-PTS, and SFLAHC-PTS technique.

The McGill Interactive Pediatric OncoGenetic Guidelines: An approach to identifying pediatric oncology patients most likely to benefit from a genetic evaluation.

PubMed

Goudie, Catherine; Coltin, Hallie; Witkowski, Leora; Mourad, Stephanie; Malkin, David; Foulkes, William D

2017-08-01

Identifying cancer predisposition syndromes in children with tumors is crucial, yet few clinical guidelines exist to identify children at high risk of having germline mutations. The McGill Interactive Pediatric OncoGenetic Guidelines project aims to create a validated pediatric guideline in the form of a smartphone/tablet application using algorithms to process clinical data and help determine whether to refer a child for genetic assessment. This paper discusses the initial stages of the project, focusing on its overall structure, the methodology underpinning the algorithms, and the upcoming algorithm validation process. © 2017 Wiley Periodicals, Inc.

Optimization of genomic selection training populations with a genetic algorithm

USDA-ARS?s Scientific Manuscript database

In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

A Model for the Stop Planning and Timetables of Customized Buses

PubMed Central

Yang, Yang

2017-01-01

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space–time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs. PMID:28056041

A Model for the Stop Planning and Timetables of Customized Buses.

PubMed

Ma, Jihui; Zhao, Yanqing; Yang, Yang; Liu, Tao; Guan, Wei; Wang, Jiao; Song, Cuiying

2017-01-01

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space-time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs.

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

NASA Astrophysics Data System (ADS)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

Fireworks algorithm for mean-VaR/CVaR models

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Liu, Zhifeng

2017-10-01

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.

Dynamic traffic assignment : genetic algorithms approach

DOT National Transportation Integrated Search

1997-01-01

Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

BEYSIK: Language description and handbook for programmers (system for the collective use of the Institute of Space Research, Academy of Sciences USSR)

NASA Technical Reports Server (NTRS)

Orlov, I. G.

1979-01-01

The BASIC algorithmic language is described, and a guide is presented for the programmer using the language interpreter. The high-level algorithm BASIC is a problem-oriented programming language intended for solution of computational and engineering problems.

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

NASA Astrophysics Data System (ADS)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Research on laser marking speed optimization by using genetic algorithm.

PubMed

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%.

Tag SNP selection via a genetic algorithm.

PubMed

Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh

2010-10-01

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.

Research on rolling element bearing fault diagnosis based on genetic algorithm matching pursuit

NASA Astrophysics Data System (ADS)

Rong, R. W.; Ming, T. F.

2017-12-01

In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.

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

Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

ERIC Educational Resources Information Center

Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

2009-01-01

Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease

PubMed Central

Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.

2008-01-01

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems.

PubMed

Liu, Chun; Kroll, Andreas

2016-01-01

Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

PubMed Central

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

PubMed

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Bridging the gap between high-throughput genetic and transcriptional data reveals cellular pathways responding to alpha-synuclein toxicity

PubMed Central

Yeger-Lotem, Esti; Riva, Laura; Su, Linhui Julie; Gitler, Aaron D.; Cashikar, Anil; King, Oliver D.; Auluck, Pavan K.; Geddie, Melissa L.; Valastyan, Julie S.; Karger, David R.; Lindquist, Susan; Fraenkel, Ernest

2009-01-01

Cells respond to stimuli by changes in various processes, including signaling pathways and gene expression. Efforts to identify components of these responses increasingly depend on mRNA profiling and genetic library screens, yet the functional roles of the genes identified by these assays often remain enigmatic. By comparing the results of these two assays across various cellular responses, we found that they are consistently distinct. Moreover, genetic screens tend to identify response regulators, while mRNA profiling frequently detects metabolic responses. We developed an integrative approach that bridges the gap between these data using known molecular interactions, thus highlighting major response pathways. We harnessed this approach to reveal cellular pathways related to alpha-synuclein, a small lipid-binding protein implicated in several neurodegenerative disorders including Parkinson disease. For this we screened an established yeast model for alpha-synuclein toxicity to identify genes that when overexpressed alter cellular survival. Application of our algorithm to these data and data from mRNA profiling provided functional explanations for many of these genes and revealed novel relations between alpha-synuclein toxicity and basic cellular pathways. PMID:19234470

Data science approaches to pharmacogenetics.

PubMed

Penrod, N M; Moore, J H

2014-01-01

Pharmacogenetic studies rely on applied statistics to evaluate genetic data describing natural variation in response to pharmacotherapeutics such as drugs and vaccines. In the beginning, these studies were based on candidate gene approaches that specifically focused on efficacy or adverse events correlated with variants of single genes. This hypothesis driven method required the researcher to have a priori knowledge of which genes or gene sets to investigate. According to rational design, the focus of these studies has been on drug metabolizing enzymes, drug transporters, and drug targets. As technology has progressed, these studies have transitioned to hypothesis-free explorations where markers across the entire genome can be measured in large scale, population based, genome-wide association studies (GWAS). This enables identification of novel genetic biomarkers, therapeutic targets, and analysis of gene-gene interactions, which may reveal molecular mechanisms of drug activities. Ultimately, the challenge is to utilize gene-drug associations to create dosing algorithms based individual genotypes, which will guide physicians and ensure they prescribe the correct dose of the correct drug the first time eliminating trial-and-error and adverse events. We review here basic concepts and applications of data science to the genetic analysis of pharmacologic outcomes.

Genetic algorithm for nuclear data evaluation

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

Arthur, Jennifer Ann

These are slides on genetic algorithm for nuclear data evaluation. The following is covered: initial population, fitness (outer loop), calculate fitness, selection (first part of inner loop), reproduction (second part of inner loop), solution, and examples.

Tailored Testing Theory and Practice: A Basic Model, Normal Ogive Submodels, and Tailored Testing Algorithms

DTIC Science & Technology

1983-08-01

ACCESSION NO «• TITLE (and Sublltle) TAILORED TESTING THEORY AND PRACTICE: A BASIC MODEL , NORMAL OGIVE SUBMODELS, AND TAILORED TESTING ALGORITHMS 7...single common-factor model , the author derives the two- and three-parametir normal ogfve il’^irTr^ functions as submodels. For both of these...PAOEfWiwi Dmia Bnfnd) NPRDC TR 83-32 AUGUST 1983 TAILORED TESTING THEORY AND PRACTICE: A BASIC MODEL , NORMAL OGIVE SUBMODELS, AND TAILORED TESTING

Innovations in human genetics education. Incorporation of genetics into a problem-based medical school curriculum.

PubMed Central

Swinford, A E; McKeag, D B

1990-01-01

There has been recent interest in the development of problem-based human genetics curricula in U.S. medical schools. The College of Human Medicine at Michigan State University has had a problem-based curriculum since 1974. The vertical integration of genetics within the problem-based curriculum, called "Track II," has recently been revised. On first inspection, the curriculum appeared to lack a significant genetics component; however, on further analysis it was found that many genetics concepts were covered in the biochemistry, microbiology, pathology, and clinical science components. Both basic science concepts and clinical applications of genetics are covered in the curriculum by providing appropriate references for basic concepts and including inherited conditions within the differential diagnosis in the cases studied. Evaluations consist of a multiple-choice content exam and a modified essay exam based on a clinical case, allowing evaluation of both basic concepts and problem-solving ability. This curriculum prepares students to use genetics in a clinical context in their future careers. PMID:2220816

[The discussion of the infiltrative model of chemical knowledge stepping into genetics teaching in agricultural institute or university].

PubMed

Zou, Ping; Luo, Pei-Gao

2010-05-01

Chemistry is an important group of basic courses, while genetics is one of the important major-basic courses in curriculum of many majors in agricultural institutes or universities. In order to establish the linkage between the major course and the basic course, the ability of application of the chemical knowledge previously learned in understanding genetic knowledge in genetics teaching is worthy of discussion for genetics teachers. In this paper, the authors advocate to apply some chemical knowledge previously learned to understand genetic knowledge in genetics teaching with infiltrative model, which could help students learn and understand genetic knowledge more deeply. Analysis of the intrinsic logistic relationship among the knowledge of different courses and construction of the integral knowledge network are useful for students to improve their analytic, comprehensive and logistic abilities. By this way, we could explore a new teaching model to develop the talents with new ideas and comprehensive competence in agricultural fields.

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

Fuel management optimization using genetic algorithms and expert knowledge

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

DeChaine, M.D.; Feltus, M.A.

1996-09-01

The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.

Optimal placement of tuning masses on truss structures by genetic algorithms

NASA Technical Reports Server (NTRS)

Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.

1993-01-01

Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

Application of a Genetic Algorithm and Multi Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the Workplace

DTIC Science & Technology

2008-06-01

postponed the fulfillment of her own Masters Degree by at least 18 months so that I would have the opportunity to earn mine. She is smart , lovely...GENETIC ALGORITHM AND MULTI AGENT SYSTEM TO EXPLORE EMERGENT PATTERNS OF SOCIAL RATIONALITY AND A DISTRESS-BASED MODEL FOR DECEIT IN THE WORKPLACE...of a Genetic Algorithm and Mutli Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Strategic Control Algorithm Development : Volume 3. Strategic Algorithm Report.

DOT National Transportation Integrated Search

1974-08-01

The strategic algorithm report presents a detailed description of the functional basic strategic control arrival algorithm. This description is independent of a particular computer or language. Contained in this discussion are the geometrical and env...

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

Algorithme intelligent d'optimisation d'un design structurel de grande envergure

NASA Astrophysics Data System (ADS)

Dominique, Stephane

The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase. Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer. The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary. Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space. The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc. These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5. Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem. One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost. To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

NASA Astrophysics Data System (ADS)

Sun, Xiuqiao; Wang, Jian

2018-07-01

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Research on Laser Marking Speed Optimization by Using Genetic Algorithm

PubMed Central

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%. PMID:25955831

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications

NASA Astrophysics Data System (ADS)

Entezari-Maleki, Reza; Movaghar, Ali

Task scheduling algorithms in grid environments strive to maximize the overall throughput of the grid. In order to maximize the throughput of the grid environments, the makespan of the grid tasks should be minimized. In this paper, a new task scheduling algorithm is proposed to assign tasks to the grid resources with goal of minimizing the total makespan of the tasks. The algorithm uses the genetic approach to find the suitable assignment within grid resources. The experimental results obtained from applying the proposed algorithm to schedule independent tasks within grid environments demonstrate the applicability of the algorithm in achieving schedules with comparatively lower makespan in comparison with other well-known scheduling algorithms such as, Min-min, Max-min, RASA and Sufferage algorithms.

Genetic Algorithms to Optimizatize Lecturer Assessment's Criteria

NASA Astrophysics Data System (ADS)

Jollyta, Deny; Johan; Hajjah, Alyauma

2017-12-01

The lecturer assessment criteria is used as a measurement of the lecturer's performance in a college environment. To determine the value for a criteriais complicated and often leads to doubt. The absence of a standard valuefor each assessment criteria will affect the final results of the assessment and become less presentational data for the leader of college in taking various policies relate to reward and punishment. The Genetic Algorithm comes as an algorithm capable of solving non-linear problems. Using chromosomes in the random initial population, one of the presentations is binary, evaluates the fitness function and uses crossover genetic operator and mutation to obtain the desired crossbreed. It aims to obtain the most optimum criteria values in terms of the fitness function of each chromosome. The training results show that Genetic Algorithm able to produce the optimal values of lecturer assessment criteria so that can be usedby the college as a standard value for lecturer assessment criteria.

A theoretical comparison of evolutionary algorithms and simulated annealing

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

Hart, W.E.

1995-08-28

This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less

Design of Genetic Algorithms for Topology Control of Unmanned Vehicles

DTIC Science & Technology

2010-01-01

decentralised topology control mechanism distributed among active running software agents to achieve a uniform spread of terrestrial unmanned vehicles...14. ABSTRACT We present genetic algorithms (GAs) as a decentralised topology control mechanism distributed among active running software agents to...inspired topology control algorithm. The topology control of UVs using a decentralised solution over an unknown geographical terrain is a challenging

Combinatorial optimization problem solution based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, Peng

2017-08-01

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.

Genetics and Common Disorders: Implications for Primary Care and Public Health Providers

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

McInerney, Joseph D.; Greendale, Karen; Peay, Holly L.

We developed this program for primary care providers (PCPs) and public health professionals (PHPs) who are interested in increasing their understanding of the genetics of common chronic diseases and of the implications of genetics and genomics for their fields. The program differs from virtually all previous educational efforts in genetics for health professionals in that it focuses on the genetics of common chronic disease and on the broad principles that emerge when one views disease from the perspectives of variation and individuality, which are at the heart of thinking genetically. The CD-ROM introduces users to content that will improve theirmore » understanding of topics such as: • A framework for genetics and common disease; • Basic information on genetics, genomics, genetic medicine, and public health genetics, all in the context of common chronic disease; • The status of research on genetic contributions to specific common diseases, including a review of research methods; • Genetic/environmental interaction as the new “central dogma” of public health genetics; • The importance of taking and analyzing a family history; • The likely impact of potential gene discovery and genetic testing on genetic counseling and risk assessment and on the practices of PCPs and PHPs; • Stratification of populations into low-, moderate-, and high-risk categories; • The potential role of PCPs and PHPs in identifying high-risk individuals and families, in providing limited genetics services, and in referring to clinical genetics specialists; the potential for standard referral algorithms; • Implications of genetic insights for diagnosis and treatment; • Ethical, legal, and social issues that arise from genetic testing for common chronic diseases; and • Specific prevention strategies based on understanding of genetics and genetic/ environmental interactions. The interactive content – developed by experts in genetics, primary care, and public health – is organized around two case studies designed to appeal to primary care providers (thrombophilia) and public health professionals (development of a screening grogram for colorectal cancer). NCHPEG has distributed more than 0000 copies of the CD-ROM to NCHPEG member organizations and to other organizations and individuals in response to requests. The program also is available at www.nchpeg.org.« less

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

PubMed Central

Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

2010-01-01

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Light-extraction enhancement for light-emitting diodes: a firefly-inspired structure refined by the genetic algorithm

NASA Astrophysics Data System (ADS)

Bay, Annick; Mayer, Alexandre

2014-09-01

The efficiency of light-emitting diodes (LED) has increased significantly over the past few years, but the overall efficiency is still limited by total internal reflections due to the high dielectric-constant contrast between the incident and emergent media. The bioluminescent organ of fireflies gave incentive for light-extraction enhance-ment studies. A specific factory-roof shaped structure was shown, by means of light-propagation simulations and measurements, to enhance light extraction significantly. In order to achieve a similar effect for light-emitting diodes, the structure needs to be adapted to the specific set-up of LEDs. In this context simulations were carried out to determine the best geometrical parameters. In the present work, the search for a geometry that maximizes the extraction of light has been conducted by using a genetic algorithm. The idealized structure considered previously was generalized to a broader variety of shapes. The genetic algorithm makes it possible to search simultaneously over a wider range of parameters. It is also significantly less time-consuming than the previous approach that was based on a systematic scan on parameters. The results of the genetic algorithm show that (1) the calculations can be performed in a smaller amount of time and (2) the light extraction can be enhanced even more significantly by using optimal parameters determined by the genetic algorithm for the generalized structure. The combination of the genetic algorithm with the Rigorous Coupled Waves Analysis method constitutes a strong simulation tool, which provides us with adapted designs for enhancing light extraction from light-emitting diodes.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

Autonomous intelligent military robots: Army ants, killer bees, and cybernetic soldiers

NASA Astrophysics Data System (ADS)

Finkelstein, Robert

The rationale for developing autonomous intelligent robots in the military is to render conventional warfare systems ineffective and indefensible. The Desert Storm operation demonstrated the effectiveness of such systems as unmanned air and ground vehicles and indicated the future possibilities of robotic technology. Robotic military vehicles would have the advantages of expendability, low cost, lower complexity compared to manned systems, survivability, maneuverability, and a capability to share in instantaneous communication and distributed processing of combat information. Basic characteristics of intelligent systems and hierarchical control systems with sensor inputs are described. Genetic algorithms are seen as a means of achieving appropriate levels of intelligence in a robotic system. Potential impacts of robotic technology in the military are outlined.

MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

EPA Science Inventory

In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...

Analysis of the HLA population data (AHPD) submitted to the 15th International Histocompatibility/Immunogenetics Workshop by using the Gene[rate] computer tools accommodating ambiguous data (AHPD project report).

PubMed

Nunes, J M; Riccio, M E; Buhler, S; Di, D; Currat, M; Ries, F; Almada, A J; Benhamamouch, S; Benitez, O; Canossi, A; Fadhlaoui-Zid, K; Fischer, G; Kervaire, B; Loiseau, P; de Oliveira, D C M; Papasteriades, C; Piancatelli, D; Rahal, M; Richard, L; Romero, M; Rousseau, J; Spiroski, M; Sulcebe, G; Middleton, D; Tiercy, J-M; Sanchez-Mazas, A

2010-07-01

During the 15th International Histocompatibility and Immunogenetics Workshop (IHIWS), 14 human leukocyte antigen (HLA) laboratories participated in the Analysis of HLA Population Data (AHPD) project where 18 new population samples were analyzed statistically and compared with data available from previous workshops. To that aim, an original methodology was developed and used (i) to estimate frequencies by taking into account ambiguous genotypic data, (ii) to test for Hardy-Weinberg equilibrium (HWE) by using a nested likelihood ratio test involving a parameter accounting for HWE deviations, (iii) to test for selective neutrality by using a resampling algorithm, and (iv) to provide explicit graphical representations including allele frequencies and basic statistics for each series of data. A total of 66 data series (1-7 loci per population) were analyzed with this standard approach. Frequency estimates were compliant with HWE in all but one population of mixed stem cell donors. Neutrality testing confirmed the observation of heterozygote excess at all HLA loci, although a significant deviation was established in only a few cases. Population comparisons showed that HLA genetic patterns were mostly shaped by geographic and/or linguistic differentiations in Africa and Europe, but not in America where both genetic drift in isolated populations and gene flow in admixed populations led to a more complex genetic structure. Overall, a fruitful collaboration between HLA typing laboratories and population geneticists allowed finding useful solutions to the problem of estimating gene frequencies and testing basic population diversity statistics on highly complex HLA data (high numbers of alleles and ambiguities), with promising applications in either anthropological, epidemiological, or transplantation studies.

Optimal reentry prediction of space objects from LEO using RSM and GA

NASA Astrophysics Data System (ADS)

Mutyalarao, M.; Raj, M. Xavier James

2012-07-01

The accurate estimation of the orbital life time (OLT) of decaying near-Earth objects is of considerable importance for the prediction of risk object re-entry time and hazard assessment as well as for mitigation strategies. Recently, due to the reentries of large number of risk objects, which poses threat to the human life and property, a great concern is developed in the space scientific community all over the World. The evolution of objects in Low Earth Orbit (LEO) is determined by a complex interplay of the perturbing forces, mainly due to atmospheric drag and Earth gravity. These orbits are mostly in low eccentric (eccentricity < 0.2) and have variations in perigee and apogee altitudes due to perturbations during a revolution. The changes in the perigee and apogee altitudes of these orbits are mainly due to the gravitational perturbations of the Earth and the atmospheric density. It has become necessary to use extremely complex force models to match with the present operational requirements and observational techniques. Further the re-entry time of the objects in such orbits is sensitive to the initial conditions. In this paper the problem of predicting re-entry time is attempted as an optimal estimation problem. It is known that the errors are more in eccentricity for the observations based on two line elements (TLEs). Thus two parameters, initial eccentricity and ballistic coefficient, are chosen for optimal estimation. These two parameters are computed with response surface method (RSM) using a genetic algorithm (GA) for the selected time zones, based on rough linear variation of response parameter, the mean semi-major axis during orbit evolution. Error minimization between the observed and predicted mean Semi-major axis is achieved by the application of an optimization algorithm such as Genetic Algorithm (GA). The basic feature of the present approach is that the model and measurement errors are accountable in terms of adjusting the ballistic coefficient and eccentricity. The methodology is tested with the recently reentered objects ROSAT and PHOBOS GRUNT satellites. The study reveals a good agreement with the actual reentry time of these objects. It is also observed that the absolute percentage error in re-entry prediction time for all the two objects is found to be very less. Keywords: low eccentric, Response surface method, Genetic algorithm, apogee altitude, Ballistic coefficient

Strain gage selection in loads equations using a genetic algorithm

NASA Technical Reports Server (NTRS)

1994-01-01

Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.

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.

Improved artificial bee colony algorithm based gravity matching navigation method.

PubMed

Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang

2014-07-18

Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.

Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method

PubMed Central

Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang

2014-01-01

Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position. PMID:25046019

Dementia diagnoses from clinical and neuropsychological data compared: the Cache County study.

PubMed

Tschanz, J T; Welsh-Bohmer, K A; Skoog, I; West, N; Norton, M C; Wyse, B W; Nickles, R; Breitner, J C

2000-03-28

To validate a neuropsychological algorithm for dementia diagnosis. We developed a neuropsychological algorithm in a sample of 1,023 elderly residents of Cache County, UT. We compared algorithmic and clinical dementia diagnoses both based on DSM-III-R criteria. The algorithm diagnosed dementia when there was impairment in memory and at least one other cognitive domain. We also tested a variant of the algorithm that incorporated functional measures that were based on structured informant reports. Of 1,023 participants, 87% could be classified by the basic algorithm, 94% when functional measures were considered. There was good concordance between basic psychometric and clinical diagnoses (79% agreement, kappa = 0.57). This improved after incorporating functional measures (90% agreement, kappa = 0.76). Neuropsychological algorithms may reasonably classify individuals on dementia status across a range of severity levels and ages and may provide a useful adjunct to clinical diagnoses in population studies.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

PubMed

Ullah, Saleem; Groen, Thomas A; Schlerf, Martin; Skidmore, Andrew K; Nieuwenhuis, Willem; Vaiphasa, Chaichoke

2012-01-01

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

PubMed Central

Rabow, A. A.; Scheraga, H. A.

1996-01-01

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints. PMID:8880904

The genetic algorithm: A robust method for stress inversion

NASA Astrophysics Data System (ADS)

Thakur, Prithvi; Srivastava, Deepak C.; Gupta, Pravin K.

2017-01-01

The stress inversion of geological or geophysical observations is a nonlinear problem. In most existing methods, it is solved by linearization, under certain assumptions. These linear algorithms not only oversimplify the problem but also are vulnerable to entrapment of the solution in a local optimum. We propose the use of a nonlinear heuristic technique, the genetic algorithm, which searches the global optimum without making any linearizing assumption or simplification. The algorithm mimics the natural evolutionary processes of selection, crossover and mutation and, minimizes a composite misfit function for searching the global optimum, the fittest stress tensor. The validity and efficacy of the algorithm are demonstrated by a series of tests on synthetic and natural fault-slip observations in different tectonic settings and also in situations where the observations are noisy. It is shown that the genetic algorithm is superior to other commonly practised methods, in particular, in those tectonic settings where none of the principal stresses is directed vertically and/or the given data set is noisy.

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

EPA Science Inventory

Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Optimization of multicast optical networks with genetic algorithm

NASA Astrophysics Data System (ADS)

Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

2007-11-01

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

Air data system optimization using a genetic algorithm

NASA Technical Reports Server (NTRS)

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

1992-01-01

An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

DOE PAGES

Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J.; ...

2014-10-15

In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

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.

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

PubMed Central

Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta

2016-01-01

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Architecture and data processing alternatives for the TSE computer. Volume 3: Execution of a parallel counting algorithm using array logic (Tse) devices

NASA Technical Reports Server (NTRS)

Metcalfe, A. G.; Bodenheimer, R. E.

1976-01-01

A parallel algorithm for counting the number of logic-l elements in a binary array or image developed during preliminary investigation of the Tse concept is described. The counting algorithm is implemented using a basic combinational structure. Modifications which improve the efficiency of the basic structure are also presented. A programmable Tse computer structure is proposed, along with a hardware control unit, Tse instruction set, and software program for execution of the counting algorithm. Finally, a comparison is made between the different structures in terms of their more important characteristics.

The Basic Principles and Methods of the System Approach to Compression of Telemetry Data

NASA Astrophysics Data System (ADS)

Levenets, A. V.

2018-01-01

The task of data compressing of measurement data is still urgent for information-measurement systems. In paper the basic principles necessary for designing of highly effective systems of compression of telemetric information are offered. A basis of the offered principles is representation of a telemetric frame as whole information space where we can find of existing correlation. The methods of data transformation and compressing algorithms realizing the offered principles are described. The compression ratio for offered compression algorithm is about 1.8 times higher, than for a classic algorithm. Thus, results of a research of methods and algorithms showing their good perspectives.

Milestone Completion Report WBS 1.3.5.05 ECP/VTK-m FY17Q4 [MS-17/03-06] Key Reduce / Spatial Division / Basic Advect / Normals STDA05-4.

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

Moreland, Kenneth D.

The FY17Q4 milestone of the ECP/VTK-m project includes the completion of a key-reduce scheduling mechanism, a spatial division algorithm, an algorithm for basic particle advection, and the computation of smoothed surface normals. With the completion of this milestone, we are able to, respectively, more easily group like elements (a common visualization algorithm operation), provide the fundamentals for geometric search structures, provide the fundamentals for many flow visualization algorithms, and provide more realistic rendering of surfaces approximated with facets.

A survey of the state-of-the-art and focused research in range systems

NASA Technical Reports Server (NTRS)

Kung, Yao; Balakrishnan, A. V.

1988-01-01

In this one-year renewal of NASA Contract No. 2-304, basic research, development, and implementation in the areas of modern estimation algorithms and digital communication systems have been performed. In the first area, basic study on the conversion of general classes of practical signal processing algorithms into systolic array algorithms is considered, producing four publications. Also studied were the finite word length effects and convergence rates of lattice algorithms, producing two publications. In the second area of study, the use of efficient importance sampling simulation technique for the evaluation of digital communication system performances were studied, producing two publications.

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

NASA Technical Reports Server (NTRS)

Juang, J. N.; Pappa, R. S.

1985-01-01

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

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.

Translational bioinformatics: linking the molecular world to the clinical world.

PubMed

Altman, R B

2012-06-01

Translational bioinformatics represents the union of translational medicine and bioinformatics. Translational medicine moves basic biological discoveries from the research bench into the patient-care setting and uses clinical observations to inform basic biology. It focuses on patient care, including the creation of new diagnostics, prognostics, prevention strategies, and therapies based on biological discoveries. Bioinformatics involves algorithms to represent, store, and analyze basic biological data, including DNA sequence, RNA expression, and protein and small-molecule abundance within cells. Translational bioinformatics spans these two fields; it involves the development of algorithms to analyze basic molecular and cellular data with an explicit goal of affecting clinical care.

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

Selecting materialized views using random algorithm

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

2007-04-01

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

Ortho Image and DTM Generation with Intelligent Methods

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadeghian, S.

2013-10-01

Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. Genetic algorithm and artificial neural network are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of genetic algorithm and artificial neural network in geospatial production process like geometric modelling of satellite images for ortho photo generation and height interpolation in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the genetic algorithm for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with genetic algorithm and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perceptron network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones. Finally the artificial intelligence methods, like genetic algorithms as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms

NASA Technical Reports Server (NTRS)

Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve

2000-01-01

A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.

Incremental cost-effectiveness of algorithm-driven genetic testing versus no testing for Maturity Onset Diabetes of the Young (MODY) in Singapore.

PubMed

Nguyen, Hai Van; Finkelstein, Eric Andrew; Mital, Shweta; Gardner, Daphne Su-Lyn

2017-11-01

Offering genetic testing for Maturity Onset Diabetes of the Young (MODY) to all young patients with type 2 diabetes has been shown to be not cost-effective. This study tests whether a novel algorithm-driven genetic testing strategy for MODY is incrementally cost-effective relative to the setting of no testing. A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing strategy and a strategy of no genetic testing over a 30-year time horizon from a payer's perspective. The algorithm uses glutamic acid decarboxylase (GAD) antibody testing (negative antibodies), age of onset of diabetes (<45 years) and body mass index (<25 kg/m 2 if diagnosed >30 years) to stratify the population of patients with diabetes into three subgroups, and testing for MODY only among the subgroup most likely to have the mutation. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model. The algorithm-driven MODY testing strategy has an incremental cost-effectiveness ratio of US$93 663 per quality-adjusted life year relative to the no testing strategy. If the price of genetic testing falls from US$1050 to US$530 (a 50% decrease), it will become cost-effective. Our proposed algorithm-driven testing strategy for MODY is not yet cost-effective based on established benchmarks. However, as genetic testing prices continue to fall, this strategy is likely to become cost-effective in the near future. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Genetic Algorithms for Multiple-Choice Problems

NASA Astrophysics Data System (ADS)

Aickelin, Uwe

2010-04-01

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

Constrained minimization of smooth functions using a genetic algorithm

NASA Technical Reports Server (NTRS)

Moerder, Daniel D.; Pamadi, Bandu N.

1994-01-01

The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

Implementation and comparative analysis of the optimisations produced by evolutionary algorithms for the parameter extraction of PSP MOSFET model

NASA Astrophysics Data System (ADS)

Hadia, Sarman K.; Thakker, R. A.; Bhatt, Kirit R.

2016-05-01

The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (ABC), variant ABC and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor field effect transistor (MOSFET) model. These algorithms are applied for the MOSFET parameter extraction problem using a Pennsylvania surface potential model. MOSFET parameter extraction procedures involve reducing the error between measured and modelled data. This study shows that ABC algorithm optimises the parameter values based on intelligent activities of honey bee swarms. Some modifications have also been applied to the basic ABC algorithm. Particle swarm optimisation is a population-based stochastic optimisation method that is based on bird flocking activities. The performances of these algorithms are compared with respect to the quality of the solutions. The simulation results of this study show that the PSO algorithm performs better than the variant ABC and basic ABC algorithm for the parameter extraction of the MOSFET model; also the implementation of the ABC algorithm is shown to be simpler than that of the PSO algorithm.

Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm

NASA Technical Reports Server (NTRS)

Baskaran, Subbiah; Noever, D.

1999-01-01

Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.

An Adaptive Immune Genetic Algorithm for Edge Detection

NASA Astrophysics Data System (ADS)

Li, Ying; Bai, Bendu; Zhang, Yanning

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

Generation of Non-Homogeneous Poisson Processes by Thinning: Programming Considerations and Comparision with Competing Algorithms.

DTIC Science & Technology

1978-12-01

Poisson processes . The method is valid for Poisson processes with any given intensity function. The basic thinning algorithm is modified to exploit several refinements which reduce computer execution time by approximately one-third. The basic and modified thinning programs are compared with the Poisson decomposition and gap-statistics algorithm, which is easily implemented for Poisson processes with intensity functions of the form exp(a sub 0 + a sub 1t + a sub 2 t-squared. The thinning programs are competitive in both execution

Genes and inheritance.

PubMed

Middelton, L A; Peters, K F

2001-10-01

The information gained from the Human Genome Project and related genetic research will undoubtedly create significant changes in healthcare practice. It is becoming increasingly clear that nurses in all areas of clinical practice will require a fundamental understanding of basic genetics. This article provides the oncology nurse with an overview of basic genetic concepts, including inheritance patterns of single gene conditions, pedigree construction, chromosome aberrations, and the multifactorial basis underlying the common diseases of adulthood. Normal gene structure and function are introduced and the biochemistry of genetic errors is described.

[The discussion of the infiltrative model of mathematical knowledge to genetics teaching].

PubMed

Liu, Jun; Luo, Pei-Gao

2011-11-01

Genetics, the core course of biological field, is an importance major-basic course in curriculum of many majors related with biology. Due to strong theoretical and practical as well as abstract of genetics, it is too difficult to study on genetics for many students. At the same time, mathematics is one of the basic courses in curriculum of the major related natural science, which has close relationship with the establishment, development and modification of genetics. In this paper, to establish the intrinsic logistic relationship and construct the integral knowledge network and to help students improving the analytic, comprehensive and logistic abilities, we applied some mathematical infiltrative model genetic knowledge in genetics teaching, which could help students more deeply learn and understand genetic knowledge.

Convergence properties of simple genetic algorithms

NASA Technical Reports Server (NTRS)

Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.

1974-01-01

The essential parameters determining the behaviour of genetic algorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.

A genetic algorithm approach in interface and surface structure optimization

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

Zhang, Jian

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the materialmore » structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.« less

Estimation of radiative and conductive properties of a semitransparent medium using genetic algorithms

NASA Astrophysics Data System (ADS)

Braiek, A.; Adili, A.; Albouchi, F.; Karkri, M.; Ben Nasrallah, S.

2016-06-01

The aim of this work is to simultaneously identify the conductive and radiative parameters of a semitransparent sample using a photothermal method associated with an inverse problem. The identification of the conductive and radiative proprieties is performed by the minimization of an objective function that represents the errors between calculated temperature and measured signal. The calculated temperature is obtained from a theoretical model built with the thermal quadrupole formalism. Measurement is obtained in the rear face of the sample whose front face is excited by a crenel of heat flux. For identification procedure, a genetic algorithm is developed and used. The genetic algorithm is a useful tool in the simultaneous estimation of correlated or nearly correlated parameters, which can be a limiting factor for the gradient-based methods. The results of the identification procedure show the efficiency and the stability of the genetic algorithm to simultaneously estimate the conductive and radiative properties of clear glass.

An application of CART algorithm in genetics: IGFs and cGH polymorphisms in Japanese quail

NASA Astrophysics Data System (ADS)

Kaplan, Selçuk

2017-04-01

The avian insulin-like growth factor-1 (IGFs) and avian growth hormone (cGH) genes are the most important genes that can affect bird performance traits because of its important function in growth and metabolism. Understanding the molecular genetic basis of variation in growth-related traits is of importance for continued improvement and increased rates of genetic gain. The objective of the present study was to identify polymorphisms of cGH and IGFs genes in Japanese quail using conventional least square method (LSM) and CART algorithm. Therefore, this study was aimed to demonstrate at determining the polymorphisms of two genes related growth characteristics via CART algorithm. A simulated data set was generated to analyze by adhering the results of some poultry genetic studies which it includes live weights at 5 weeks of age, 3 alleles and 6 genotypes of cGH and 2 alleles and 3 genotypes of IGFs. As a result, it has been determined that the CART algorithm has some advantages as for that LSM.

Application of artificial intelligence to search ground-state geometry of clusters

NASA Astrophysics Data System (ADS)

Lemes, Maurício Ruv; Marim, L. R.; dal Pino, A.

2002-08-01

We introduce a global optimization procedure, the neural-assisted genetic algorithm (NAGA). It combines the power of an artificial neural network (ANN) with the versatility of the genetic algorithm. This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space. If a reasonable amount of data is available, the ANN can ``understand'' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution. We tested the method in a search for the ground-state geometry of silicon clusters. We trained the ANN with information about the geometry and energetics of small silicon clusters. Next, the ANN learned how to restrict the configurational space for larger silicon clusters. For Si10 and Si20, we noticed that the NAGA is at least three times faster than the ``pure'' genetic algorithm. As the size of the cluster increases, it is expected that the gain in terms of time will increase as well.

Application of genetic algorithms to focal mechanism determination

NASA Astrophysics Data System (ADS)

Kobayashi, Reiji; Nakanishi, Ichiro

1994-04-01

Genetic algorithms are a new class of methods for global optimization. They resemble Monte Carlo techniques, but search for solutions more efficiently than uniform Monte Carlo sampling. In the field of geophysics, genetic algorithms have recently been used to solve some non-linear inverse problems (e.g., earthquake location, waveform inversion, migration velocity estimation). We present an application of genetic algorithms to focal mechanism determination from first-motion polarities of P-waves and apply our method to two recent large events, the Kushiro-oki earthquake of January 15, 1993 and the SW Hokkaido (Japan Sea) earthquake of July 12, 1993. Initial solution and curvature information of the objective function that gradient methods need are not required in our approach. Moreover globally optimal solutions can be efficiently obtained. Calculation of polarities based on double-couple models is the most time-consuming part of the source mechanism determination. The amount of calculations required by the method designed in this study is much less than that of previous grid search methods.

Optimized design on condensing tubes high-speed TIG welding technology magnetic control based on genetic algorithm

NASA Astrophysics Data System (ADS)

Lu, Lin; Chang, Yunlong; Li, Yingmin; Lu, Ming

2013-05-01

An orthogonal experiment was conducted by the means of multivariate nonlinear regression equation to adjust the influence of external transverse magnetic field and Ar flow rate on welding quality in the process of welding condenser pipe by high-speed argon tungsten-arc welding (TIG for short). The magnetic induction and flow rate of Ar gas were used as optimum variables, and tensile strength of weld was set to objective function on the base of genetic algorithm theory, and then an optimal design was conducted. According to the request of physical production, the optimum variables were restrained. The genetic algorithm in the MATLAB was used for computing. A comparison between optimum results and experiment parameters was made. The results showed that the optimum technologic parameters could be chosen by the means of genetic algorithm with the conditions of excessive optimum variables in the process of high-speed welding. And optimum technologic parameters of welding coincided with experiment results.

Optimal sensor placement for spatial lattice structure based on genetic algorithms

NASA Astrophysics Data System (ADS)

Liu, Wei; Gao, Wei-cheng; Sun, Yi; Xu, Min-jian

2008-10-01

Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.

Multidisciplinary design optimization of the belt drive system considering both structure and vibration characteristics based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Yuan, Yongliang; Song, Xueguan; Sun, Wei; Wang, Xiaobang

2018-05-01

The dynamic performance of a belt drive system is composed of many factors, such as the efficiency, the vibration, and the optimal parameters. The conventional design only considers the basic performance of the belt drive system, while ignoring its overall performance. To address all these challenges, the study on vibration characteristics and optimization strategies could be a feasible way. This paper proposes a new optimization strategy and takes a belt drive design optimization as a case study based on the multidisciplinary design optimization (MDO). The MDO of the belt drive system is established and the corresponding sub-systems are analyzed. The multidisciplinary optimization is performed by using an improved genetic algorithm. Based on the optimal results obtained from the MDO, the three-dimension (3D) model of the belt drive system is established for dynamics simulation by virtual prototyping. From the comparison of the results with respect to different velocities and loads, the MDO method can effectively reduce the transverse vibration amplitude. The law of the vibration displacement, the vibration frequency, and the influence of velocities on the transverse vibrations has been obtained. Results show that the MDO method is of great help to obtain the optimal structural parameters. Furthermore, the kinematics principle of the belt drive has been obtained. The belt drive design case indicates that the proposed method in this paper can also be used to solve other engineering optimization problems efficiently.

Determination of optimum allocation and pricing of distributed generation using genetic algorithm methodology

NASA Astrophysics Data System (ADS)

Mwakabuta, Ndaga Stanslaus

Electric power distribution systems play a significant role in providing continuous and "quality" electrical energy to different classes of customers. In the context of the present restrictions on transmission system expansions and the new paradigm of "open and shared" infrastructure, new approaches to distribution system analyses, economic and operational decision-making need investigation. This dissertation includes three layers of distribution system investigations. In the basic level, improved linear models are shown to offer significant advantages over previous models for advanced analysis. In the intermediate level, the improved model is applied to solve the traditional problem of operating cost minimization using capacitors and voltage regulators. In the advanced level, an artificial intelligence technique is applied to minimize cost under Distributed Generation injection from private vendors. Soft computing techniques are finding increasing applications in solving optimization problems in large and complex practical systems. The dissertation focuses on Genetic Algorithm for investigating the economic aspects of distributed generation penetration without compromising the operational security of the distribution system. The work presents a methodology for determining the optimal pricing of distributed generation that would help utilities make a decision on how to operate their system economically. This would enable modular and flexible investments that have real benefits to the electric distribution system. Improved reliability for both customers and the distribution system in general, reduced environmental impacts, increased efficiency of energy use, and reduced costs of energy services are some advantages.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.

2014-01-01

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.

Prioritizing the Components of Vulnerability: A Genetic Algorithm Minimization of Flood Risk

NASA Astrophysics Data System (ADS)

Bongolan, Vena Pearl; Ballesteros, Florencio; Baritua, Karessa Alexandra; Junne Santos, Marie

2013-04-01

We define a flood resistant city as an optimal arrangement of communities according to their traits, with the goal of minimizing the flooding vulnerability via a genetic algorithm. We prioritize the different components of flooding vulnerability, giving each component a weight, thus expressing vulnerability as a weighted sum. This serves as the fitness function for the genetic algorithm. We also allowed non-linear interactions among related but independent components, viz, poverty and mortality rate, and literacy and radio/ tv penetration. The designs produced reflect the relative importance of the components, and we observed a synchronicity between the interacting components, giving us a more consistent design.

Algorithmic Trading with Developmental and Linear Genetic Programming

NASA Astrophysics Data System (ADS)

Wilson, Garnett; Banzhaf, Wolfgang

A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

Supervised chaos genetic algorithm based state of charge determination for LiFePO4 batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Shen, Yanqing

2018-04-01

LiFePO4 battery is developed rapidly in electric vehicle, whose safety and functional capabilities are influenced greatly by the evaluation of available cell capacity. Added with adaptive switch mechanism, this paper advances a supervised chaos genetic algorithm based state of charge determination method, where a combined state space model is employed to simulate battery dynamics. The method is validated by the experiment data collected from battery test system. Results indicate that the supervised chaos genetic algorithm based state of charge determination method shows great performance with less computation complexity and is little influenced by the unknown initial cell state.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

PubMed

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Hudec, Ján; Gramatová, Elena

2015-07-01

The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Fast optimization of glide vehicle reentry trajectory based on genetic algorithm

NASA Astrophysics Data System (ADS)

Jia, Jun; Dong, Ruixing; Yuan, Xuejun; Wang, Chuangwei

2018-02-01

An optimization method of reentry trajectory based on genetic algorithm is presented to meet the need of reentry trajectory optimization for glide vehicle. The dynamic model for the glide vehicle during reentry period is established. Considering the constraints of heat flux, dynamic pressure, overload etc., the optimization of reentry trajectory is investigated by utilizing genetic algorithm. The simulation shows that the method presented by this paper is effective for the optimization of reentry trajectory of glide vehicle. The efficiency and speed of this method is comparative with the references. Optimization results meet all constraints, and the on-line fast optimization is potential by pre-processing the offline samples.

On Directly Solving SCHRÖDINGER Equation for H+2 Ion by Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saha, Rajendra; Bhattacharyya, S. P.

Schrödinger equation (SE) is sought to be solved directly for the ground state of H+2 ion by invoking genetic algorithm (GA). In one approach the internuclear distance (R) is kept fixed, the corresponding electronic SE for H+2 is solved by GA at each R and the full potential energy curve (PEC) is constructed. The minimum of the PEC is then located giving Ve and Re. Alternatively, Ve and Re are located in a single run by allowing R to vary simultaneously while solving the electronic SE by genetic algorithm. The performance patterns of the two strategies are compared.

Applying a Genetic Algorithm to Reconfigurable Hardware

NASA Technical Reports Server (NTRS)

Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim

2004-01-01

This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

Mobile transporter path planning

NASA Technical Reports Server (NTRS)

Baffes, Paul; Wang, Lui

1990-01-01

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.

Improved teaching-learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

NASA Astrophysics Data System (ADS)

Buddala, Raviteja; Mahapatra, Siba Sankar

2017-11-01

Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.

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.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)

NASA Astrophysics Data System (ADS)

Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman

2012-01-01

In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

PubMed

Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo

2018-06-25

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.

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.

Distributed query plan generation using multiobjective genetic algorithm.

PubMed

Panicker, Shina; Kumar, T V Vijay

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali

2013-04-01

The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

PubMed Central

Panicker, Shina; Vijay Kumar, T. V.

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

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

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

PubMed

Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

NASA Astrophysics Data System (ADS)

Fronita, Mona; Gernowo, Rahmat; Gunawan, Vincencius

2018-02-01

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

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.

iNJclust: Iterative Neighbor-Joining Tree Clustering Framework for Inferring Population Structure.

PubMed

Limpiti, Tulaya; Amornbunchornvej, Chainarong; Intarapanich, Apichart; Assawamakin, Anunchai; Tongsima, Sissades

2014-01-01

Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

NASA Astrophysics Data System (ADS)

Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita

2018-03-01

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

A CAL Program to Teach the Basic Principles of Genetic Engineering--A Change from the Traditional Approach.

ERIC Educational Resources Information Center

Dewhurst, D. G.; And Others

1989-01-01

An interactive computer-assisted learning program written for the BBC microcomputer to teach the basic principles of genetic engineering is described. Discussed are the hardware requirements software, use of the program, and assessment. (Author/CW)

Genetic Screening: A Unique Game of Survival

ERIC Educational Resources Information Center

Kurvink, Karen; Bowser, Jessica

2004-01-01

A creative learning game that helps students reinforce basic genetic information and facilitate the identification and understanding of the more subtle issues is presented. The basic framework of the game was conceived by a business major taking non-biology major course 'heredity and society-intertwining legacy.

On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms.

PubMed

Gobin, Oliver C; Schüth, Ferdi

2008-01-01

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

NASA Astrophysics Data System (ADS)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

Anthropomorphic robot for recognition and drawing generalized object images

NASA Astrophysics Data System (ADS)

Ginzburg, Vera M.

1998-10-01

The process of recognition, for instance, understanding the text, written by different fonts, consists in the depriving of the individual attributes of the letters in the particular font. It is shown that such process, in Nature and technique, can be provided by the narrowing the spatial frequency of the object's image by its defocusing. In defocusing images remain only areas, so-called Informative Fragments (IFs), which all together form the generalized (stylized) image of many identical objects. It is shown that the variety of shapes of IFs is restricted and can be presented by `Geometrical alphabet'. The `letters' for this alphabet can be created using two basic `genetic' figures: a stripe and round spot. It is known from physiology that the special cells of visual cortex response to these particular figures. The prototype of such `genetic' alphabet has been made using Boolean algebra (Venn's diagrams). The algorithm for drawing the letter's (`genlet's') shape in this alphabet and generalized images of objects (for example, `sleeping cat'), are given. A scheme of an anthropomorphic robot is shown together with results of model computer experiment of the robot's action--`drawing' the generalized image.

Colony image acquisition and genetic segmentation algorithm and colony analyses

NASA Astrophysics Data System (ADS)

Wang, W. X.

2012-01-01

Colony anaysis is used in a large number of engineerings such as food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing. In order to reduce laboring and increase analysis acuracy, many researchers and developers have made efforts for image analysis systems. The main problems in the systems are image acquisition, image segmentation and image analysis. In this paper, to acquire colony images with good quality, an illumination box was constructed. In the box, the distances between lights and dishe, camra lens and lights, and camera lens and dishe are adjusted optimally. In image segmentation, It is based on a genetic approach that allow one to consider the segmentation problem as a global optimization,. After image pre-processing and image segmentation, the colony analyses are perfomed. The colony image analysis consists of (1) basic colony parameter measurements; (2) colony size analysis; (3) colony shape analysis; and (4) colony surface measurements. All the above visual colony parameters can be selected and combined together, used to make a new engineeing parameters. The colony analysis can be applied into different applications.

Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method

ERIC Educational Resources Information Center

Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen

2008-01-01

In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Rakoczy, John; Steincamp, James; Taylor, Jaime

2003-01-01

A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida (Published Proceedings)

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Genetic algorithm to solve the problems of lectures and practicums scheduling

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

New Genetics

MedlinePlus

... Century-Old Evolutionary Puzzle Computing Genetics Model Organisms RNA Interference The New Genetics is a science education ... the basics of DNA and its molecular cousin RNA, and new directions in genetic research. The New ...

Organic Rankine Cycle for Residual Heat to Power Conversion in Natural Gas Compressor Station. Part I: Modelling and Optimisation Framework

NASA Astrophysics Data System (ADS)

Chaczykowski, Maciej

2016-06-01

Basic organic Rankine cycle (ORC), and two variants of regenerative ORC have been considered for the recovery of exhaust heat from natural gas compressor station. The modelling framework for ORC systems has been presented and the optimisation of the systems was carried out with turbine power output as the variable to be maximized. The determination of ORC system design parameters was accomplished by means of the genetic algorithm. The study was aimed at estimating the thermodynamic potential of different ORC configurations with several working fluids employed. The first part of this paper describes the ORC equipment models which are employed to build a NLP formulation to tackle design problems representative for waste energy recovery on gas turbines driving natural gas pipeline compressors.

Multiple feature fusion via covariance matrix for visual tracking

NASA Astrophysics Data System (ADS)

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui

2018-04-01

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

A Solution Method of Job-shop Scheduling Problems by the Idle Time Shortening Type Genetic Algorithm

NASA Astrophysics Data System (ADS)

Ida, Kenichi; Osawa, Akira

In this paper, we propose a new idle time shortening method for Job-shop scheduling problems (JSPs). We insert its method into a genetic algorithm (GA). The purpose of JSP is to find a schedule with the minimum makespan. We suppose that it is effective to reduce idle time of a machine in order to improve the makespan. The left shift is a famous algorithm in existing algorithms for shortening idle time. The left shift can not arrange the work to idle time. For that reason, some idle times are not shortened by the left shift. We propose two kinds of algorithms which shorten such idle time. Next, we combine these algorithms and the reversal of a schedule. We apply GA with its algorithm to benchmark problems and we show its effectiveness.

Packing Boxes into Multiple Containers Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Menghani, Deepak; Guha, Anirban

2016-07-01

Container loading problems have been studied extensively in the literature and various analytical, heuristic and metaheuristic methods have been proposed. This paper presents two different variants of a genetic algorithm framework for the three-dimensional container loading problem for optimally loading boxes into multiple containers with constraints. The algorithms are designed so that it is easy to incorporate various constraints found in real life problems. The algorithms are tested on data of standard test cases from literature and are found to compare well with the benchmark algorithms in terms of utilization of containers. This, along with the ability to easily incorporate a wide range of practical constraints, makes them attractive for implementation in real life scenarios.

Airport Flight Departure Delay Model on Improved BN Structure Learning

NASA Astrophysics Data System (ADS)

Cao, Weidong; Fang, Xiangnong

An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

PubMed Central

Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

2014-01-01

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy.

PubMed

Xing, KeYi; Han, LiBin; Zhou, MengChu; Wang, Feng

2012-06-01

Deadlock-free control and scheduling are vital for optimizing the performance of automated manufacturing systems (AMSs) with shared resources and route flexibility. Based on the Petri net models of AMSs, this paper embeds the optimal deadlock avoidance policy into the genetic algorithm and develops a novel deadlock-free genetic scheduling algorithm for AMSs. A possible solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the one-step look-ahead method in the optimal deadlock control policy, the feasibility of a chromosome is checked, and infeasible chromosomes are amended into feasible ones, which can be easily decoded into a feasible deadlock-free schedule. The chromosome representation and polynomial complexity of checking and amending procedures together support the cooperative aspect of genetic search for scheduling problems strongly.

Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book.

PubMed

Sadygov, Rovshan G; Cociorva, Daniel; Yates, John R

2004-12-01

Database searching is an essential element of large-scale proteomics. Because these methods are widely used, it is important to understand the rationale of the algorithms. Most algorithms are based on concepts first developed in SEQUEST and PeptideSearch. Four basic approaches are used to determine a match between a spectrum and sequence: descriptive, interpretative, stochastic and probability-based matching. We review the basic concepts used by most search algorithms, the computational modeling of peptide identification and current challenges and limitations of this approach for protein identification.

Development of a pneumatic high-angle-of-attack Flush Airdata Sensing (HI-FADS) system

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Moes, Timothy R.; Leondes, Cornelius T.

1992-01-01

The HI-FADS system design is an evolution of the FADS systems (e.g., Larson et al., 1980, 1987), which emphasizes the entire airdata system development. This paper describes the HI-FADS measurement system, with particular consideration given to the basic measurement hardware and the development of the HI-FADS aerodynamic model and the basic nonlinear regression algorithm. Algorithm initialization techniques are developed, and potential algorithm divergence problems are discussed. Data derived from HI-FADS flight tests are used to demonstrate the system accuracies and to illustrate the developed concepts and methods.

Implementing a Multiple Criteria Model Base in Co-Op with a Graphical User Interface Generator

DTIC Science & Technology

1993-09-23

PROMETHEE ................................ 44 A. THE ALGORITHM S ................................... 44 1. Basic Algorithm of PROMETHEE I and... PROMETHEE II ..... 45 a. Use of the Algorithm in PROMETHEE I ............. 49 b. Use of the Algorithm in PROMETHEE II ............. 50 V 2. Algorithm of... PROMETHEE V ......................... 50 B. SCREEN DESIGNS OF PROMETHEE ...................... 51 1. PROMETHEE I and PROMETHEE II ................... 52 a

Evaluating a hybrid web-based basic genetics course for health professionals.

PubMed

Wallen, Gwenyth R; Cusack, Georgie; Parada, Suzan; Miller-Davis, Claiborne; Cartledge, Tannia; Yates, Jan

2011-08-01

Health professionals, particularly nurses, continue to struggle with the expanding role of genetics information in the care of their patients. This paper describes an evaluation study of the effectiveness of a hybrid basic genetics course for healthcare professionals combining web-based learning with traditional face-to-face instructional techniques. A multidisciplinary group from the National Institutes of Health (NIH) created "Basic Genetics Education for Healthcare Providers" (BGEHCP). This program combined 7 web-based self-education modules with monthly traditional face-to-face lectures by genetics experts. The course was pilot tested by 186 healthcare providers from various disciplines with 69% (n=129) of the class registrants enrolling in a pre-post evaluation trial. Outcome measures included critical thinking knowledge items and a Web-based Learning Environment Inventory (WEBLEI). Results indicated a significant (p<0.001) change in knowledge scores. WEBLEI scores indicated program effectiveness particularly in the area of convenience, access and the course structure and design. Although significant increases in overall knowledge scores were achieved, scores in content areas surrounding genetic risk identification and ethical issues regarding genetic testing reflected continued gaps in knowledge. Web-based genetics education may help overcome genetics knowledge deficits by providing access for health professionals with diverse schedules in a variety of national and international settings. Published by Elsevier Ltd.

Nuclear fuel management optimization using genetic algorithms

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

DeChaine, M.D.; Feltus, M.A.

1995-07-01

The code independent genetic algorithm reactor optimization (CIGARO) system has been developed to optimize nuclear reactor loading patterns. It uses genetic algorithms (GAs) and a code-independent interface, so any reactor physics code (e.g., CASMO-3/SIMULATE-3) can be used to evaluate the loading patterns. The system is compared to other GA-based loading pattern optimizers. Tests were carried out to maximize the beginning of cycle k{sub eff} for a pressurized water reactor core loading with a penalty function to limit power peaking. The CIGARO system performed well, increasing the k{sub eff} after lowering the peak power. Tests of a prototype parallel evaluation methodmore » showed the potential for a significant speedup.« less

Medical image segmentation using genetic algorithms.

PubMed

Maulik, Ujjwal

2009-03-01

Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.

Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

PubMed

Manning, Timmy; Sleator, Roy D; Walsh, Paul

2013-01-01

For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods.

PubMed

Ozdemir, Durmus; Dinc, Erdal

2004-07-01

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.

Stochastic search in structural optimization - Genetic algorithms and simulated annealing

NASA Technical Reports Server (NTRS)

Hajela, Prabhat

1993-01-01

An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

Unscented Sampling Techniques For Evolutionary Computation With Applications To Astrodynamic Optimization

DTIC Science & Technology

2016-09-01

to both genetic algorithms and evolution strategies to achieve these goals. The results of this research offer a promising new set of modified ...abs_all.jsp?arnumber=203904 [163] Z. Michalewicz, C. Z. Janikow, and J. B. Krawczyk, “A modified genetic algo- rithm for optimal control problems...Available: http://arc.aiaa.org/doi/abs/10.2514/ 2.7053 375 [166] N. Yokoyama and S. Suzuki, “ Modified genetic algorithm for constrained trajectory

A novel structure-aware sparse learning algorithm for brain imaging genetics.

PubMed

Du, Lei; Jingwen, Yan; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

2014-01-01

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

A Smart Itsy Bitsy Spider for the Web.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chung, Yi-Ming; Ramsey, Marshall; Yang, Christopher C.

1998-01-01

This study tested two Web personal spiders (i.e., agents that take users' requests and perform real-time customized searches) based on best first-search and genetic-algorithm techniques. Both results were comparable and complementary, although the genetic algorithm obtained higher recall value. The Java-based interface was found to be necessary…

Genetic algorithm driven spectral shaping of supercontinuum radiation in a photonic crystal fiber

NASA Astrophysics Data System (ADS)

Michaeli, Linor; Bahabad, Alon

2018-05-01

We employ a genetic algorithm to control a pulse-shaping system pumping a nonlinear photonic crystal with ultrashort pulses. With this system, we are able to modify the spectrum of the generated supercontinuum (SC) radiation to yield narrow Gaussian-like features around pre-selected wavelengths over the whole SC spectrum.

Genetic Influences on Learning Disabilties I: Clinical Genetics.

ERIC Educational Resources Information Center

Smith, Shelley D.; Pennington, Bruce F.

1983-01-01

A discussion of basic genetic principles is followed by a review of selected genetic syndromes involving learning disabilites (such as Noonan Syndrome, Neurofibromatosis, Pheuylketonuria, and cleft lip and palate). Guidelines for securing a genetic evaluation are given. (CL)

MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

PubMed Central

Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

2014-01-01

The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

NASA Astrophysics Data System (ADS)

Sen, G.; Akyol, E.

2010-04-01

The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

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.

Optimization of fuels from waste composition with application of genetic algorithm.

PubMed

Małgorzata, Wzorek

2014-05-01

The objective of this article is to elaborate a method to optimize the composition of the fuels from sewage sludge (PBS fuel - fuel based on sewage sludge and coal slime, PBM fuel - fuel based on sewage sludge and meat and bone meal, PBT fuel - fuel based on sewage sludge and sawdust). As a tool for an optimization procedure, the use of a genetic algorithm is proposed. The optimization task involves the maximization of mass fraction of sewage sludge in a fuel developed on the basis of quality-based criteria for the use as an alternative fuel used by the cement industry. The selection criteria of fuels composition concerned such parameters as: calorific value, content of chlorine, sulphur and heavy metals. Mathematical descriptions of fuel compositions and general forms of the genetic algorithm, as well as the obtained optimization results are presented. The results of this study indicate that the proposed genetic algorithm offers an optimization tool, which could be useful in the determination of the composition of fuels that are produced from waste.

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Routine Discovery of Complex Genetic Models using Genetic Algorithms

PubMed Central

Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.

2010-01-01

Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983

A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

ERIC Educational Resources Information Center

Chahine, Firas Safwan

2012-01-01

Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN

EPA Science Inventory

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

Genetics Home Reference: recurrent hydatidiform mole

MedlinePlus

... Rashid Y, Sheridan E, Bonthron DT. Genetic and epigenetic analysis of recurrent hydatidiform mole. Hum Mutat. 2009 ... on PubMed Nguyen NM, Slim R. Genetics and Epigenetics of Recurrent Hydatidiform Moles: Basic Science and Genetic ...

Genetic algorithms for multicriteria shape optimization of induction furnace

NASA Astrophysics Data System (ADS)

Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

2012-09-01

In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

Design of synthetic biological logic circuits based on evolutionary algorithm.

PubMed

Chuang, Chia-Hua; Lin, Chun-Liang; Chang, Yen-Chang; Jennawasin, Tanagorn; Chen, Po-Kuei

2013-08-01

The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real-structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis-regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer-based modelling technology has been verified showing its great advantages in the purpose.

Preliminary Structural Design Using Topology Optimization with a Comparison of Results from Gradient and Genetic Algorithm Methods

NASA Technical Reports Server (NTRS)

Burt, Adam O.; Tinker, Michael L.

2014-01-01

In this paper, genetic algorithm based and gradient-based topology optimization is presented in application to a real hardware design problem. Preliminary design of a planetary lander mockup structure is accomplished using these methods that prove to provide major weight savings by addressing the structural efficiency during the design cycle. This paper presents two alternative formulations of the topology optimization problem. The first is the widely-used gradient-based implementation using commercially available algorithms. The second is formulated using genetic algorithms and internally developed capabilities. These two approaches are applied to a practical design problem for hardware that has been built, tested and proven to be functional. Both formulations converged on similar solutions and therefore were proven to be equally valid implementations of the process. This paper discusses both of these formulations at a high level.

Inverting the parameters of an earthquake-ruptured fault with a genetic algorithm

NASA Astrophysics Data System (ADS)

Yu, Ting-To; Fernàndez, Josè; Rundle, John B.

1998-03-01

Natural selection is the spirit of the genetic algorithm (GA): by keeping the good genes in the current generation, thereby producing better offspring during evolution. The crossover function ensures the heritage of good genes from parent to offspring. Meanwhile, the process of mutation creates a special gene, the character of which does not exist in the parent generation. A program based on genetic algorithms using C language is constructed to invert the parameters of an earthquake-ruptured fault. The verification and application of this code is shown to demonstrate its capabilities. It is determined that this code is able to find the global extreme and can be used to solve more practical problems with constraints gathered from other sources. It is shown that GA is superior to other inverting schema in many aspects. This easy handling and yet powerful algorithm should have many suitable applications in the field of geosciences.

Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

NASA Astrophysics Data System (ADS)

Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

2017-04-01

Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

An Improved SoC Test Scheduling Method Based on Simulated Annealing Algorithm

NASA Astrophysics Data System (ADS)

Zheng, Jingjing; Shen, Zhihang; Gao, Huaien; Chen, Bianna; Zheng, Weida; Xiong, Xiaoming

2017-02-01

In this paper, we propose an improved SoC test scheduling method based on simulated annealing algorithm (SA). It is our first to disorganize IP core assignment for each TAM to produce a new solution for SA, allocate TAM width for each TAM using greedy algorithm and calculate corresponding testing time. And accepting the core assignment according to the principle of simulated annealing algorithm and finally attain the optimum solution. Simultaneously, we run the test scheduling experiment with the international reference circuits provided by International Test Conference 2002(ITC’02) and the result shows that our algorithm is superior to the conventional integer linear programming algorithm (ILP), simulated annealing algorithm (SA) and genetic algorithm(GA). When TAM width reaches to 48,56 and 64, the testing time based on our algorithm is lesser than the classic methods and the optimization rates are 30.74%, 3.32%, 16.13% respectively. Moreover, the testing time based on our algorithm is very close to that of improved genetic algorithm (IGA), which is state-of-the-art at present.

A new compound arithmetic crossover-based genetic algorithm for constrained optimisation in enterprise systems

NASA Astrophysics Data System (ADS)

Jin, Chenxia; Li, Fachao; Tsang, Eric C. C.; Bulysheva, Larissa; Kataev, Mikhail Yu

2017-01-01

In many real industrial applications, the integration of raw data with a methodology can support economically sound decision-making. Furthermore, most of these tasks involve complex optimisation problems. Seeking better solutions is critical. As an intelligent search optimisation algorithm, genetic algorithm (GA) is an important technique for complex system optimisation, but it has internal drawbacks such as low computation efficiency and prematurity. Improving the performance of GA is a vital topic in academic and applications research. In this paper, a new real-coded crossover operator, called compound arithmetic crossover operator (CAC), is proposed. CAC is used in conjunction with a uniform mutation operator to define a new genetic algorithm CAC10-GA. This GA is compared with an existing genetic algorithm (AC10-GA) that comprises an arithmetic crossover operator and a uniform mutation operator. To judge the performance of CAC10-GA, two kinds of analysis are performed. First the analysis of the convergence of CAC10-GA is performed by the Markov chain theory; second, a pair-wise comparison is carried out between CAC10-GA and AC10-GA through two test problems available in the global optimisation literature. The overall comparative study shows that the CAC performs quite well and the CAC10-GA defined outperforms the AC10-GA.

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions.

PubMed

Vandecasteele, Frederik P J; Hess, Thomas F; Crawford, Ronald L

2007-07-01

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.

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.

Genetic algorithms for protein threading.

PubMed

Yadgari, J; Amir, A; Unger, R

1998-01-01

Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

Clustering for Binary Data Sets by Using Genetic Algorithm-Incremental K-means

NASA Astrophysics Data System (ADS)

Saharan, S.; Baragona, R.; Nor, M. E.; Salleh, R. M.; Asrah, N. M.

2018-04-01

This research was initially driven by the lack of clustering algorithms that specifically focus in binary data. To overcome this gap in knowledge, a promising technique for analysing this type of data became the main subject in this research, namely Genetic Algorithms (GA). For the purpose of this research, GA was combined with the Incremental K-means (IKM) algorithm to cluster the binary data streams. In GAIKM, the objective function was based on a few sufficient statistics that may be easily and quickly calculated on binary numbers. The implementation of IKM will give an advantage in terms of fast convergence. The results show that GAIKM is an efficient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. In conclusion, the GAIKM outperformed other clustering algorithms such as GCUK, IKM, Scalable K-means (SKM) and K-means clustering and paves the way for future research involving missing data and outliers.

A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

PubMed Central

Tsai, Chun-Wei; Tseng, Shih-Pang; Yang, Chu-Sing

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA. PMID:24892038

A high-performance genetic algorithm: using traveling salesman problem as a case.

PubMed

Tsai, Chun-Wei; Tseng, Shih-Pang; Chiang, Ming-Chao; Yang, Chu-Sing; Hong, Tzung-Pei

2014-01-01

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

Evaluation of Residual Static Corrections by Hybrid Genetic Algorithm Steepest Ascent Autostatics Inversion.Application southern Algerian fields

NASA Astrophysics Data System (ADS)

Eladj, Said; bansir, fateh; ouadfeul, sid Ali

2016-04-01

The application of genetic algorithm starts with an initial population of chromosomes representing a "model space". Chromosome chains are preferentially Reproduced based on Their fitness Compared to the total population. However, a good chromosome has a Greater opportunity to Produce offspring Compared To other chromosomes in the population. The advantage of the combination HGA / SAA is the use of a global search approach on a large population of local maxima to Improve Significantly the performance of the method. To define the parameters of the Hybrid Genetic Algorithm Steepest Ascent Auto Statics (HGA / SAA) job, we Evaluated by testing in the first stage of "Steepest Ascent," the optimal parameters related to the data used. 1- The number of iterations "Number of hill climbing iteration" is equal to 40 iterations. This parameter defines the participation of the algorithm "SA", in this hybrid approach. 2- The minimum eigenvalue for SA '= 0.8. This is linked to the quality of data and S / N ratio. To find an implementation performance of hybrid genetic algorithms in the inversion for estimating of the residual static corrections, tests Were Performed to determine the number of generation of HGA / SAA. Using the values of residual static corrections already calculated by the Approaches "SAA and CSAA" learning has Proved very effective in the building of the cross-correlation table. To determine the optimal number of generation, we Conducted a series of tests ranging from [10 to 200] generations. The application on real seismic data in southern Algeria allowed us to judge the performance and capacity of the inversion with this hybrid method "HGA / SAA". This experience Clarified the influence of the corrections quality estimated from "SAA / CSAA" and the optimum number of generation hybrid genetic algorithm "HGA" required to have a satisfactory performance. Twenty (20) generations Were enough to Improve continuity and resolution of seismic horizons. This Will allow us to achieve a more accurate structural interpretation Key words: Hybrid Genetic Algorithm, number of generations, model space, local maxima, Number of hill climbing iteration, Minimum eigenvalue, cross-correlation table

Genetic algorithm with maximum-minimum crossover (GA-MMC) applied in optimization of radiation pattern control of phased-array radars for rocket tracking systems.

PubMed

Silva, Leonardo W T; Barros, Vitor F; Silva, Sandro G

2014-08-18

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.

Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems

PubMed Central

Silva, Leonardo W. T.; Barros, Vitor F.; Silva, Sandro G.

2014-01-01

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence. PMID:25196013

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.

Refined Genetic Algorithms for Polypeptide Structure Prediction.

DTIC Science & Technology

1996-12-01

16 I I I. Algorithm Analysis, Design , and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 18 3.1 Analysis...21 3.2 Algorithm Design and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 22 3.2.1...26 IV. Exp erimen t Design

Comparing genetic algorithm and particle swarm optimization for solving capacitated vehicle routing problem

NASA Astrophysics Data System (ADS)

Iswari, T.; Asih, A. M. S.

2018-04-01

In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.

Basic test framework for the evaluation of text line segmentation and text parameter extraction.

PubMed

Brodić, Darko; Milivojević, Dragan R; Milivojević, Zoran

2010-01-01

Text line segmentation is an essential stage in off-line optical character recognition (OCR) systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms.

Basic Test Framework for the Evaluation of Text Line Segmentation and Text Parameter Extraction

PubMed Central

Brodić, Darko; Milivojević, Dragan R.; Milivojević, Zoran

2010-01-01

Text line segmentation is an essential stage in off-line optical character recognition (OCR) systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms. PMID:22399932

Planning Readings: A Comparative Exploration of Basic Algorithms

ERIC Educational Resources Information Center

Piater, Justus H.

2009-01-01

Conventional introduction to computer science presents individual algorithmic paradigms in the context of specific, prototypical problems. To complement this algorithm-centric instruction, this study additionally advocates problem-centric instruction. I present an original problem drawn from students' life that is simply stated but provides rich…

Scatter-Reducing Sounding Filtration Using a Genetic Algorithm and Mean Monthly Standard Deviation

NASA Technical Reports Server (NTRS)

Mandrake, Lukas

2013-01-01

Retrieval algorithms like that used by the Orbiting Carbon Observatory (OCO)-2 mission generate massive quantities of data of varying quality and reliability. A computationally efficient, simple method of labeling problematic datapoints or predicting soundings that will fail is required for basic operation, given that only 6% of the retrieved data may be operationally processed. This method automatically obtains a filter designed to reduce scatter based on a small number of input features. Most machine-learning filter construction algorithms attempt to predict error in the CO2 value. By using a surrogate goal of Mean Monthly STDEV, the goal is to reduce the retrieved CO2 scatter rather than solving the harder problem of reducing CO2 error. This lends itself to improved interpretability and performance. This software reduces the scatter of retrieved CO2 values globally based on a minimum number of input features. It can be used as a prefilter to reduce the number of soundings requested, or as a post-filter to label data quality. The use of the MMS (Mean Monthly Standard deviation) provides a much cleaner, clearer filter than the standard ABS(CO2-truth) metrics previously employed by competitor methods. The software's main strength lies in a clearer (i.e., fewer features required) filter that more efficiently reduces scatter in retrieved CO2 rather than focusing on the more complex (and easily removed) bias issues.

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Conceptual versus Algorithmic Learning in High School Chemistry: The Case of Basic Quantum Chemical Concepts--Part 1. Statistical Analysis of a Quantitative Study

ERIC Educational Resources Information Center

Papaphotis, Georgios; Tsaparlis, Georgios

2008-01-01

Part 1 of the findings are presented of a quantitative study (n = 125) on basic quantum chemical concepts taught in the twelfth grade (age 17-18 years) in Greece. A paper-and-pencil test of fourteen questions was used. The study compared performance in five questions that tested recall of knowledge or application of algorithmic procedures (type-A…

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

PubMed Central

Cao, Leilei; Xu, Lihong; Goodman, Erik D.

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

PubMed

Cao, Leilei; Xu, Lihong; Goodman, Erik D

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.

PubMed

Lao, Oscar; Liu, Fan; Wollstein, Andreas; Kayser, Manfred

2014-02-01

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.

Comparative Analysis of Rank Aggregation Techniques for Metasearch Using Genetic Algorithm

ERIC Educational Resources Information Center

Kaur, Parneet; Singh, Manpreet; Singh Josan, Gurpreet

2017-01-01

Rank Aggregation techniques have found wide applications for metasearch along with other streams such as Sports, Voting System, Stock Markets, and Reduction in Spam. This paper presents the optimization of rank lists for web queries put by the user on different MetaSearch engines. A metaheuristic approach such as Genetic algorithm based rank…

Finding Patterns of Emergence in Science and Technology

DTIC Science & Technology

2012-09-24

formal evaluation scheduled – Case Studies, Eight Examples: Tissue Engineering, Cold Fusion, RF Metamaterials, DNA Microarrays, Genetic Algorithms, RNAi...emerging capabilities Case Studies, Eight Examples: • Tissue Engineering, Cold Fusion, RF Metamaterials, DNA Microarrays, Genetic Algorithms...Evidence Quality (i.e., the rubric ) and deliver comprehensible evidential support for nomination • Demonstrate proof-of-concept nomination for Chinese

Validation Test Report for a Genetic Algorithm in the Glider Observation STrategies (GOST 1.0) Project: Sensitivity Studies

DTIC Science & Technology

2012-08-15

Environmental Model ( GDEM ) 72 levels) was conserved in the interpolated profiles and small variations in the vertical field may have lead to large...Planner ETKF Ensemble Transform Kalman Filter G8NCOM 1/8⁰ Global NCOM GA Genetic Algorithm GDEM Generalized Digital Environmental Model GOST

Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

2003-01-01

Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

Process optimization of rolling for zincked sheet technology using response surface methodology and genetic algorithm

NASA Astrophysics Data System (ADS)

Ji, Liang-Bo; Chen, Fang

2017-07-01

Numerical simulation and intelligent optimization technology were adopted for rolling and extrusion of zincked sheet. By response surface methodology (RSM), genetic algorithm (GA) and data processing technology, an efficient optimization of process parameters for rolling of zincked sheet was investigated. The influence trend of roller gap, rolling speed and friction factor effects on reduction rate and plate shortening rate were analyzed firstly. Then a predictive response surface model for comprehensive quality index of part was created using RSM. Simulated and predicted values were compared. Through genetic algorithm method, the optimal process parameters for the forming of rolling were solved. They were verified and the optimum process parameters of rolling were obtained. It is feasible and effective.

Genetic Algorithm Design of a 3D Printed Heat Sink

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

Wu, Tong; Ozpineci, Burak; Ayers, Curtis William

2016-01-01

In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate themore » performance of the newly designed heat sinkcompared to commercially available heat sinks.« less

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

A genetic algorithm solution to the unit commitment problem

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

Kazarlis, S.A.; Bakirtzis, A.G.; Petridis, V.

1996-02-01

This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple Ga algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 unitsmore » and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.« less

Preliminary Design of a Manned Nuclear Electric Propulsion Vehicle Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Irwin, Ryan W.; Tinker, Michael L.

2005-01-01

Nuclear electric propulsion (NEP) vehicles will be needed for future manned missions to Mars and beyond. Candidate designs must be identified for further detailed design from a large array of possibilities. Genetic algorithms have proven their utility in conceptual design studies by effectively searching a large design space to pinpoint unique optimal designs. This research combined analysis codes for NEP subsystems with a genetic algorithm. The use of penalty functions with scaling ratios was investigated to increase computational efficiency. Also, the selection of design variables for optimization was considered to reduce computation time without losing beneficial design search space. Finally, trend analysis of a reference mission to the asteroids yielded a group of candidate designs for further analysis.

Evaluation of Genetic Algorithm Concepts using Model Problems. Part 1; Single-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.

Design and evaluation of basic standard encryption algorithm modules using nanosized complementary metal oxide semiconductor molecular circuits

NASA Astrophysics Data System (ADS)

Masoumi, Massoud; Raissi, Farshid; Ahmadian, Mahmoud; Keshavarzi, Parviz

2006-01-01

We are proposing that the recently proposed semiconductor-nanowire-molecular architecture (CMOL) is an optimum platform to realize encryption algorithms. The basic modules for the advanced encryption standard algorithm (Rijndael) have been designed using CMOL architecture. The performance of this design has been evaluated with respect to chip area and speed. It is observed that CMOL provides considerable improvement over implementation with regular CMOS architecture even with a 20% defect rate. Pseudo-optimum gate placement and routing are provided for Rijndael building blocks and the possibility of designing high speed, attack tolerant and long key encryptions are discussed.

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

NASA Technical Reports Server (NTRS)

Cull, R. C.; Eltimsahy, A. H.

1983-01-01

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

Basic properties of lattices of cubes, algorithms for their construction, and application capabilities in discrete optimization

NASA Astrophysics Data System (ADS)

Khachaturov, R. V.

2015-01-01

The basic properties of a new type of lattices—a lattice of cubes—are described. It is shown that, with a suitable choice of union and intersection operations, the set of all subcubes of an N-cube forms a lattice, which is called a lattice of cubes. Algorithms for constructing such lattices are described, and the results produced by these algorithms in the case of lattices of various dimensions are illustrated. It is proved that a lattice of cubes is a lattice with supplements, which makes it possible to minimize and maximize supermodular functions on it. Examples of such functions are given. The possibility of applying previously developed efficient optimization algorithms to the formulation and solution of new classes of problems on lattices of cubes.

Imaging Genetics

ERIC Educational Resources Information Center

Munoz, Karen E.; Hyde, Luke W.; Hariri, Ahmad R.

2009-01-01

Imaging genetics is an experimental strategy that integrates molecular genetics and neuroimaging technology to examine biological mechanisms that mediate differences in behavior and the risks for psychiatric disorder. The basic principles in imaging genetics and the development of the field are discussed.

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

Use of genetic algorithms to improve the solid waste collection service in an urban area.

PubMed

Buenrostro-Delgado, Otoniel; Ortega-Rodriguez, Juan Manuel; Clemitshaw, Kevin C; González-Razo, Carlos; Hernández-Paniagua, Iván Y

2015-07-01

Increasing generation of Urban Solid Waste (USW) has become a significant issue in developing countries due to unprecedented population growth and high rates of urbanisation. This issue has exceeded current plans and programs of local governments to manage and dispose of USW. In this study, a Genetic Algorithm for Rule-set Production (GARP) integrated into a Geographic Information System (GIS) was used to find areas with socio-economic conditions that are representative of the generation of USW constituents in such areas. Socio-economic data of selected variables categorised by Basic Geostatistical Areas (BGAs) were taken from the 2000 National Population Census (NPC). USW and additional socio-economic data were collected during two survey campaigns in 1998 and 2004. Areas for sampling of USW were stratified into lower, middle and upper economic strata according to income. Data on USW constituents were analysed using descriptive statistics and Multivariate Analysis. ARC View 3.2 was used to convert the USW data and socio-economic variables to spatial data. Desk-top GARP software was run to generate a spatial model to identify areas with similar socio-economic conditions to those sampled. Results showed that socio-economic variables such as monthly income and education are positively correlated with waste constituents generated. The GARP used in this study revealed BGAs with similar socio-economic conditions to those sampled, where a similar composition of waste constituents generated is expected. Our results may be useful to decrease USW management costs by improving the collection services. Copyright © 2015 Elsevier Ltd. All rights reserved.

Materials Discovery | Materials Science | NREL

Science.gov Websites

measurement methods and specialized analysis algorithms. Projects Basic Research The basic research projects applications using high-throughput combinatorial research methods. Email | 303-384-6467 Photo of John Perkins

Web mining in soft computing framework: relevance, state of the art and future directions.

PubMed

Pal, S K; Talwar, V; Mitra, P

2002-01-01

The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art. The reason for considering Web mining, a separate field from data mining, is explained. The limitations of some of the existing Web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) are highlighted. A survey of the existing literature on "soft Web mining" is provided along with the commercially available systems. The prospective areas of Web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft Web mining" systems is explained. An extensive bibliography is also provided.

Genetics in the art and art in genetics.

PubMed

Bukvic, Nenad; Elling, John W

2015-01-15

"Healing is best accomplished when art and science are conjoined, when body and spirit are probed together", says Bernard Lown, in his book "The Lost Art of Healing". Art has long been a witness to disease either through diseases which affected artists or diseases afflicting objects of their art. In particular, artists have often portrayed genetic disorders and malformations in their work. Sometimes genetic disorders have mystical significance; other times simply have intrinsic interest. Recognizing genetic disorders is also an art form. From the very beginning of my work as a Medical Geneticist I have composed personal "algorithms" to piece together evidence of genetics syndromes and diseases from the observable signs and symptoms. In this paper we apply some 'gestalt' Genetic Syndrome Diagnostic algorithms to virtual patients found in some art masterpieces. In some the diagnosis is clear and in others the artists' depiction only supports a speculative differential diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

Production scheduling and rescheduling with genetic algorithms.

PubMed

Bierwirth, C; Mattfeld, D C

1999-01-01

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.

Basic Research in Digital Stochastic Model Algorithmic Control.

DTIC Science & Technology

1980-11-01

IDCOM Description 115 8.2 Basic Control Computation 117 8.3 Gradient Algorithm 119 8.4 Simulation Model 119 8.5 Model Modifications 123 8.6 Summary 124...constraints, and 3) control traJectorv comouta- tion. 2.1.1 Internal Model of the System The multivariable system to be controlled is represented by a...more flexible and adaptive, since the model , criteria, and sampling rates can be adjusted on-line. This flexibility comes from the use of the impulse

Conceptual versus Algorithmic Learning in High School Chemistry: The Case of Basic Quantum Chemical Concepts--Part 2. Students' Common Errors, Misconceptions and Difficulties in Understanding

ERIC Educational Resources Information Center

Papaphotis, Georgios; Tsaparlis, Georgios

2008-01-01

Part 2 of the findings are presented of a quantitative study (n = 125) on basic quantum chemical concepts taught at twelfth grade (age 17-18 years) in Greece. A paper-and-pencil test of fourteen questions was used that were of two kinds: five questions that tested recall of knowledge or application of algorithmic procedures (type-A questions);…

GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

PubMed

Pérez-Castillo, Yunierkis; Lazar, Cosmin; Taminau, Jonatan; Froeyen, Mathy; Cabrera-Pérez, Miguel Ángel; Nowé, Ann

2012-09-24

Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

Genetic algorithms for the vehicle routing problem

NASA Astrophysics Data System (ADS)

Volna, Eva

2016-06-01

The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.

Analysis of basic clustering algorithms for numerical estimation of statistical averages in biomolecules.

PubMed

Anandakrishnan, Ramu; Onufriev, Alexey

2008-03-01

In statistical mechanics, the equilibrium properties of a physical system of particles can be calculated as the statistical average over accessible microstates of the system. In general, these calculations are computationally intractable since they involve summations over an exponentially large number of microstates. Clustering algorithms are one of the methods used to numerically approximate these sums. The most basic clustering algorithms first sub-divide the system into a set of smaller subsets (clusters). Then, interactions between particles within each cluster are treated exactly, while all interactions between different clusters are ignored. These smaller clusters have far fewer microstates, making the summation over these microstates, tractable. These algorithms have been previously used for biomolecular computations, but remain relatively unexplored in this context. Presented here, is a theoretical analysis of the error and computational complexity for the two most basic clustering algorithms that were previously applied in the context of biomolecular electrostatics. We derive a tight, computationally inexpensive, error bound for the equilibrium state of a particle computed via these clustering algorithms. For some practical applications, it is the root mean square error, which can be significantly lower than the error bound, that may be more important. We how that there is a strong empirical relationship between error bound and root mean square error, suggesting that the error bound could be used as a computationally inexpensive metric for predicting the accuracy of clustering algorithms for practical applications. An example of error analysis for such an application-computation of average charge of ionizable amino-acids in proteins-is given, demonstrating that the clustering algorithm can be accurate enough for practical purposes.

MRI segmentation using dialectical optimization.

PubMed

dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E

2009-01-01

Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T(1)- and T(2)-weighted synthetic brain images of 181 slices with 1 mm, resolution of 1 mm(3), for a normal brain and a noiseless MR tomographic system without field inhomogeneities, amounting a total of 543 images, generated by the simulator BrainWeb [2]. Our principal target here is comparing our proposal to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.

Social Media: Menagerie of Metrics

DTIC Science & Technology

2010-01-27

intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm . An EA...Cloning - 22 Animals were cloned to date; genetic algorithms can help prediction (e.g. “elitism” - attempts to ensure selection by including performers...28, 2010 Evolutionary Algorithm • Evolutionary algorithm From Wikipedia, the free encyclopedia Artificial intelligence portal In artificial

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

2000-01-01

We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

Optimizing simulated fertilizer additions using a genetic algorithm with a nutrient uptake model

Treesearch

Wendell P. Cropper; N.B. Comerford

2005-01-01

Intensive management of pine plantations in the southeastern coastal plain typically involves weed and pest control, and the addition of fertilizer to meet the high nutrient demand of rapidly growing pines. In this study we coupled a mechanistic nutrient uptake model (SSAND, soil supply and nutrient demand) with a genetic algorithm (GA) in order to estimate the minimum...

Towards a Definition of Basic Numeracy

ERIC Educational Resources Information Center

Girling, Michael

1977-01-01

The author redefines basic numeracy as the ability to use a four-function calculator sensibly. He then defines "sensibly" and considers the place of algorithms in the scheme of mathematical calculations. (MN)

Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Marghany, Maged

2016-10-01

In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven

2010-05-01

Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.

A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks

PubMed Central

Camacho-Vallejo, José-Fernando; Mar-Ortiz, Julio; López-Ramos, Francisco; Rodríguez, Ricardo Pedraza

2015-01-01

Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower’s problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach. PMID:26102502

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

PubMed

Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

2015-01-01

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Genetic algorithms used for the optimization of light-emitting diodes and solar thermal collectors

NASA Astrophysics Data System (ADS)

Mayer, Alexandre; Bay, Annick; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

2014-09-01

We present a genetic algorithm (GA) we developed for the optimization of light-emitting diodes (LED) and solar thermal collectors. The surface of a LED can be covered by periodic structures whose geometrical and material parameters must be adjusted in order to maximize the extraction of light. The optimization of these parameters by the GA enabled us to get a light-extraction efficiency η of 11.0% from a GaN LED (for comparison, the flat material has a light-extraction efficiency η of only 3.7%). The solar thermal collector we considered consists of a waffle-shaped Al substrate with NiCrOx and SnO2 conformal coatings. We must in this case maximize the solar absorption α while minimizing the thermal emissivity ɛ in the infrared. A multi-objective genetic algorithm has to be implemented in this case in order to determine optimal geometrical parameters. The parameters we obtained using the multi-objective GA enable α~97.8% and ɛ~4.8%, which improves results achieved previously when considering a flat substrate. These two applications demonstrate the interest of genetic algorithms for addressing complex problems in physics.

Locating Critical Circular and Unconstrained Failure Surface in Slope Stability Analysis with Tailored Genetic Algorithm

NASA Astrophysics Data System (ADS)

Pasik, Tomasz; van der Meij, Raymond

2017-12-01

This article presents an efficient search method for representative circular and unconstrained slip surfaces with the use of the tailored genetic algorithm. Searches for unconstrained slip planes with rigid equilibrium methods are yet uncommon in engineering practice, and little publications regarding truly free slip planes exist. The proposed method presents an effective procedure being the result of the right combination of initial population type, selection, crossover and mutation method. The procedure needs little computational effort to find the optimum, unconstrained slip plane. The methodology described in this paper is implemented using Mathematica. The implementation, along with further explanations, is fully presented so the results can be reproduced. Sample slope stability calculations are performed for four cases, along with a detailed result interpretation. Two cases are compared with analyses described in earlier publications. The remaining two are practical cases of slope stability analyses of dikes in Netherlands. These four cases show the benefits of analyzing slope stability with a rigid equilibrium method combined with a genetic algorithm. The paper concludes by describing possibilities and limitations of using the genetic algorithm in the context of the slope stability problem.

Optimization of HAART with genetic algorithms and agent-based models of HIV infection.

PubMed

Castiglione, F; Pappalardo, F; Bernaschi, M; Motta, S

2007-12-15

Highly Active AntiRetroviral Therapies (HAART) can prolong life significantly to people infected by HIV since, although unable to eradicate the virus, they are quite effective in maintaining control of the infection. However, since HAART have several undesirable side effects, it is considered useful to suspend the therapy according to a suitable schedule of Structured Therapeutic Interruptions (STI). In the present article we describe an application of genetic algorithms (GA) aimed at finding the optimal schedule for a HAART simulated with an agent-based model (ABM) of the immune system that reproduces the most significant features of the response of an organism to the HIV-1 infection. The genetic algorithm helps in finding an optimal therapeutic schedule that maximizes immune restoration, minimizes the viral count and, through appropriate interruptions of the therapy, minimizes the dose of drug administered to the simulated patient. To validate the efficacy of the therapy that the genetic algorithm indicates as optimal, we ran simulations of opportunistic diseases and found that the selected therapy shows the best survival curve among the different simulated control groups. A version of the C-ImmSim simulator is available at http://www.iac.cnr.it/~filippo/c-ImmSim.html

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

NASA Technical Reports Server (NTRS)

Prasanth, Ravi K.; Markin, Robert E.; Whitaker, Kevin W.

1991-01-01

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.

Flexible Job-Shop Scheduling with Dual-Resource Constraints to Minimize Tardiness Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Paksi, A. B. N.; Ma'ruf, A.

2016-02-01

In general, both machines and human resources are needed for processing a job on production floor. However, most classical scheduling problems have ignored the possible constraint caused by availability of workers and have considered only machines as a limited resource. In addition, along with production technology development, routing flexibility appears as a consequence of high product variety and medium demand for each product. Routing flexibility is caused by capability of machines that offers more than one machining process. This paper presents a method to address scheduling problem constrained by both machines and workers, considering routing flexibility. Scheduling in a Dual-Resource Constrained shop is categorized as NP-hard problem that needs long computational time. Meta-heuristic approach, based on Genetic Algorithm, is used due to its practical implementation in industry. Developed Genetic Algorithm uses indirect chromosome representative and procedure to transform chromosome into Gantt chart. Genetic operators, namely selection, elitism, crossover, and mutation are developed to search the best fitness value until steady state condition is achieved. A case study in a manufacturing SME is used to minimize tardiness as objective function. The algorithm has shown 25.6% reduction of tardiness, equal to 43.5 hours.

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.

A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping

NASA Astrophysics Data System (ADS)

Jiang, Wenbo; Wang, Jun; Dong, Xiucheng

2013-02-01

In this paper, a novel hybrid algorithm for the design of a phase diffractive optical elements (PDOE) is proposed. It combines the genetic algorithm (GA) with the transformable scale BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, the penalty function was used in the cost function definition. The novel hybrid algorithm has the global merits of the genetic algorithm as well as the local improvement capabilities of the transformable scale BFGS algorithm. We designed the PDOE using the conventional simulated annealing algorithm and the novel hybrid algorithm. To compare the performance of two algorithms, three indexes of the diffractive efficiency, uniformity error and the signal-to-noise ratio are considered in numerical simulation. The results show that the novel hybrid algorithm has good convergence property and good stability. As an application example, the PDOE was used for the Gaussian beam shaping; high diffractive efficiency, low uniformity error and high signal-to-noise were obtained. The PDOE can be used for high quality beam shaping such as inertial confinement fusion (ICF), excimer laser lithography, fiber coupling laser diode array, laser welding, etc. It shows wide application value.

Application of artificial neural networks and genetic algorithms to modeling molecular electronic spectra in solution

NASA Astrophysics Data System (ADS)

Lilichenko, Mark; Kelley, Anne Myers

2001-04-01

A novel approach is presented for finding the vibrational frequencies, Franck-Condon factors, and vibronic linewidths that best reproduce typical, poorly resolved electronic absorption (or fluorescence) spectra of molecules in condensed phases. While calculation of the theoretical spectrum from the molecular parameters is straightforward within the harmonic oscillator approximation for the vibrations, "inversion" of an experimental spectrum to deduce these parameters is not. Standard nonlinear least-squares fitting methods such as Levenberg-Marquardt are highly susceptible to becoming trapped in local minima in the error function unless very good initial guesses for the molecular parameters are made. Here we employ a genetic algorithm to force a broad search through parameter space and couple it with the Levenberg-Marquardt method to speed convergence to each local minimum. In addition, a neural network trained on a large set of synthetic spectra is used to provide an initial guess for the fitting parameters and to narrow the range searched by the genetic algorithm. The combined algorithm provides excellent fits to a variety of single-mode absorption spectra with experimentally negligible errors in the parameters. It converges more rapidly than the genetic algorithm alone and more reliably than the Levenberg-Marquardt method alone, and is robust in the presence of spectral noise. Extensions to multimode systems, and/or to include other spectroscopic data such as resonance Raman intensities, are straightforward.

A novel pipeline based FPGA implementation of a genetic algorithm

NASA Astrophysics Data System (ADS)

Thirer, Nonel

2014-05-01

To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.

Advancing X-ray scattering metrology using inverse genetic algorithms.

PubMed

Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph

2016-01-01

We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm

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

Patton, Robert M; Beckerman, Barbara G; Potok, Thomas E

2009-01-01

Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns in radiology reports that utilizes a genetic algorithm (GA) as the learning method. The approach described here successfully learned cue phrase patterns for two distinct classes of radiology reports. These patterns can then be used as a basis for automatically categorizing, clustering, ormore » retrieving relevant data for the user.« less

Genetic and Genomic Toolbox of Zea mays

PubMed Central

Nannas, Natalie J.; Dawe, R. Kelly

2015-01-01

Maize has a long history of genetic and genomic tool development and is considered one of the most accessible higher plant systems. With a fully sequenced genome, a suite of cytogenetic tools, methods for both forward and reverse genetics, and characterized phenotype markers, maize is amenable to studying questions beyond plant biology. Major discoveries in the areas of transposons, imprinting, and chromosome biology came from work in maize. Moving forward in the post-genomic era, this classic model system will continue to be at the forefront of basic biological study. In this review, we outline the basics of working with maize and describe its rich genetic toolbox. PMID:25740912

Branch-pipe-routing approach for ships using improved genetic algorithm

NASA Astrophysics Data System (ADS)

Sui, Haiteng; Niu, Wentie

2016-09-01

Branch-pipe routing plays fundamental and critical roles in ship-pipe design. The branch-pipe-routing problem is a complex combinatorial optimization problem and is thus difficult to solve when depending only on human experts. A modified genetic-algorithm-based approach is proposed in this paper to solve this problem. The simplified layout space is first divided into threedimensional (3D) grids to build its mathematical model. Branch pipes in layout space are regarded as a combination of several two-point pipes, and the pipe route between two connection points is generated using an improved maze algorithm. The coding of branch pipes is then defined, and the genetic operators are devised, especially the complete crossover strategy that greatly accelerates the convergence speed. Finally, simulation tests demonstrate the performance of proposed method.

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.

Modeling the Volcanic Source at Long Valley, CA, Using a Genetic Algorithm Technique

NASA Technical Reports Server (NTRS)

Tiampo, Kristy F.

1999-01-01

In this project, we attempted to model the deformation pattern due to the magmatic source at Long Valley caldera using a real-value coded genetic algorithm (GA) inversion similar to that found in Michalewicz, 1992. The project has been both successful and rewarding. The genetic algorithm, coded in the C programming language, performs stable inversions over repeated trials, with varying initial and boundary conditions. The original model used a GA in which the geophysical information was coded into the fitness function through the computation of surface displacements for a Mogi point source in an elastic half-space. The program was designed to invert for a spherical magmatic source - its depth, horizontal location and volume - using the known surface deformations. It also included the capability of inverting for multiple sources.

Evolutionary Optimization of Yagi-Uda Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Colombano, Silvano P.

2001-01-01

Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

NASA Astrophysics Data System (ADS)

Phan, Duoc T.; Lim, James B. P.; Sha, Wei; Siew, Calvin Y. M.; Tanyimboh, Tiku T.; Issa, Honar K.; Mohammad, Fouad A.

2013-04-01

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes. A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.

Optimizing management of the condensing heat and cooling of gases compression in oxy block using of a genetic algorithm

NASA Astrophysics Data System (ADS)

Brzęczek, Mateusz; Bartela, Łukasz

2013-12-01

This paper presents the parameters of the reference oxy combustion block operating with supercritical steam parameters, equipped with an air separation unit and a carbon dioxide capture and compression installation. The possibility to recover the heat in the analyzed power plant is discussed. The decision variables and the thermodynamic functions for the optimization algorithm were identified. The principles of operation of genetic algorithm and methodology of conducted calculations are presented. The sensitivity analysis was performed for the best solutions to determine the effects of the selected variables on the power and efficiency of the unit. Optimization of the heat recovery from the air separation unit, flue gas condition and CO2 capture and compression installation using genetic algorithm was designed to replace the low-pressure section of the regenerative water heaters of steam cycle in analyzed unit. The result was to increase the power and efficiency of the entire power plant.

Teacher-to-Teacher: An Annotated Bibliography on DNA and Genetic Engineering.

ERIC Educational Resources Information Center

Mertens, Thomas R., Comp.

1984-01-01

Presented is an annotated bibliography of 24 books on DNA and genetic engineering. Areas considered in these books include: basic biological concepts to help understand advances in genetic engineering; applications of genetic engineering; social, legal, and moral issues of genetic engineering; and historical aspects leading to advances in…

Integrating population genetics and conservation biology in the era of genomics.

PubMed

Ouborg, N Joop

2010-02-23

As one of the final activities of the ESF-CONGEN Networking programme, a conference entitled 'Integrating Population Genetics and Conservation Biology' was held at Trondheim, Norway, from 23 to 26 May 2009. Conference speakers and poster presenters gave a display of the state-of-the-art developments in the field of conservation genetics. Over the five-year running period of the successful ESF-CONGEN Networking programme, much progress has been made in theoretical approaches, basic research on inbreeding depression and other genetic processes associated with habitat fragmentation and conservation issues, and with applying principles of conservation genetics in the conservation of many species. Future perspectives were also discussed in the conference, and it was concluded that conservation genetics is evolving into conservation genomics, while at the same time basic and applied research on threatened species and populations from a population genetic point of view continues to be emphasized.

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

Optimised analytical models of the dielectric properties of biological tissue.

PubMed

Salahuddin, Saqib; Porter, Emily; Krewer, Finn; O' Halloran, Martin

2017-05-01

The interaction of electromagnetic fields with the human body is quantified by the dielectric properties of biological tissues. These properties are incorporated into complex numerical simulations using parametric models such as Debye and Cole-Cole, for the computational investigation of electromagnetic wave propagation within the body. These parameters can be acquired through a variety of optimisation algorithms to achieve an accurate fit to measured data sets. A number of different optimisation techniques have been proposed, but these are often limited by the requirement for initial value estimations or by the large overall error (often up to several percentage points). In this work, a novel two-stage genetic algorithm proposed by the authors is applied to optimise the multi-pole Debye parameters for 54 types of human tissues. The performance of the two-stage genetic algorithm has been examined through a comparison with five other existing algorithms. The experimental results demonstrate that the two-stage genetic algorithm produces an accurate fit to a range of experimental data and efficiently out-performs all other optimisation algorithms under consideration. Accurate values of the three-pole Debye models for 54 types of human tissues, over 500 MHz to 20 GHz, are also presented for reference. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Algorithm Building and Learning Programming Languages Using a New Educational Paradigm

NASA Astrophysics Data System (ADS)

Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel

2011-08-01

This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.

A Case Study: Optimal Stage Gauge NetworkUsing Multi Objective Genetic Algorithm

NASA Astrophysics Data System (ADS)

Joo, H. J.; Han, D.; Jung, J.; Kim, H. S.

2017-12-01

Recently, the possibility of occurrence of localized strong heavy rainfall due to climate change is increasing and flood damage is also increasing trend in Korea. Therefore we need more precise hydrologic analysis for preparing alternatives or measures for flood reduction by considering climate conditions which we have difficulty in the prediction. To do this, obtaining reliable hydrologic data, for an example, stage data, is very important. However, the existing stage gauge stations are scattered around the country, making it difficult to maintain them in a stable manner, and subsequently hard to acquire the hydrologic data that could be used for reflecting the localized hydrologic characteristics. In order to overcome such restrictions, this paper not only aims to establish a plan to acquire the water stage data in a constant and proper manner by using limited manpower and costs, but also establishes the fundamental technology for acquiring the water level observation data or the stage data. For that, this paper identifies the current status of the stage gauge stations installed in the Chung-Ju dam in Han river, Korea and extract the factors related to the division and characteristics of basins. Then, the obtained factors are used to develop the representative unit hydrograph that shows the characteristics of flow. After that, the data are converted into the probability density function and the stations at individual basins are selected by using the entropy theory. In last step, we establish the optimized stage gauge network by the location of the stage station and grade using the Multi Objective Genetic Algorithm(MOGA) technique that takes into account for the combinations of the number of the stations. It is expected that this paper can help establish an optimal observational network of stage guages as it can be applied usefully not only for protecting against floods in a stable manner, but also for acquiring the hydrologic data in an efficient manner. Keywords : Unit Hydrograph, Entropy, Grade of Stage Gauge Station, Multi Objective Genetic Algorithm(MOGA), Optimal Stage Guage Network Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)

Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research.

PubMed

Manolio, Teri A; Fowler, Douglas M; Starita, Lea M; Haendel, Melissa A; MacArthur, Daniel G; Biesecker, Leslie G; Worthey, Elizabeth; Chisholm, Rex L; Green, Eric D; Jacob, Howard J; McLeod, Howard L; Roden, Dan; Rodriguez, Laura Lyman; Williams, Marc S; Cooper, Gregory M; Cox, Nancy J; Herman, Gail E; Kingsmore, Stephen; Lo, Cecilia; Lutz, Cathleen; MacRae, Calum A; Nussbaum, Robert L; Ordovas, Jose M; Ramos, Erin M; Robinson, Peter N; Rubinstein, Wendy S; Seidman, Christine; Stranger, Barbara E; Wang, Haoyi; Westerfield, Monte; Bult, Carol

2017-03-23

Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations, we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing. Published by Elsevier Inc.

Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research

PubMed Central

Manolio, Teri A.; Fowler, Douglas M.; Starita, Lea M.; Haendel, Melissa A.; MacArthur, Daniel G.; Biesecker, Leslie G.; Worthey, Elizabeth; Chisholm, Rex L.; Green, Eric D.; Jacob, Howard J.; McLeod, Howard L.; Roden, Dan; Rodriguez, Laura Lyman; Williams, Marc S.; Cooper, Gregory M.; Cox, Nancy J.; Herman, Gail E.; Kingsmore, Stephen; Lo, Cecilia; Lutz, Cathleen; MacRae, Calum A.; Nussbaum, Robert L.; Ordovas, Jose M.; Ramos, Erin M.; Robinson, Peter N.; Rubinstein, Wendy S.; Seidman, Christine; Stranger, Barbara E.; Wang, Haoyi; Westerfield, Monte; Bult, Carol

2017-01-01

Summary Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing. PMID:28340351

A Food Chain Algorithm for Capacitated Vehicle Routing Problem with Recycling in Reverse Logistics

NASA Astrophysics Data System (ADS)

Song, Qiang; Gao, Xuexia; Santos, Emmanuel T.

2015-12-01

This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm.

Linking the potato genome to the conserved ortholog set (COS) markers

PubMed Central

2013-01-01

Background Conserved ortholog set (COS) markers are an important functional genomics resource that has greatly improved orthology detection in Asterid species. A comprehensive list of these markers is available at Sol Genomics Network (http://solgenomics.net/) and many of these have been placed on the genetic maps of a number of solanaceous species. Results We amplified over 300 COS markers from eight potato accessions involving two diploid landraces of Solanum tuberosum Andigenum group (formerly classified as S. goniocalyx, S. phureja), and a dihaploid clone derived from a modern tetraploid cultivar of S. tuberosum and the wild species S. berthaultii, S. chomatophilum, and S. paucissectum. By BLASTn (Basic Local Alignment Search Tool of the NCBI, National Center for Biotechnology Information) algorithm we mapped the DNA sequences of these markers into the potato genome sequence. Additionally, we mapped a subset of these markers genetically in potato and present a comparison between the physical and genetic locations of these markers in potato and in comparison with the genetic location in tomato. We found that most of the COS markers are single-copy in the reference genome of potato and that the genetic location in tomato and physical location in potato sequence are mostly in agreement. However, we did find some COS markers that are present in multiple copies and those that map in unexpected locations. Sequence comparisons between species show that some of these markers may be paralogs. Conclusions The sequence-based physical map becomes helpful in identification of markers for traits of interest thereby reducing the number of markers to be tested for applications like marker assisted selection, diversity, and phylogenetic studies. PMID:23758607

Data-Driven Property Estimation for Protective Clothing

DTIC Science & Technology

2014-09-01

reliable predictions falls under the rubric “machine learning”. Inspired by the applications of machine learning in pharmaceutical drug design and...using genetic algorithms, for instance— descriptor selection can be automated as well. A well-known structured learning technique—Artificial Neural...descriptors automatically, by iteration, e.g., using a genetic algorithm [49]. 4.2.4 Avoiding Overfitting A peril of all regression—least squares as

Genetic Algorithm Optimization of Phononic Bandgap Structures

DTIC Science & Technology

2006-09-01

a GA with a computational finite element method for solving the acoustic wave equation, and find optimal designs for both metal-matrix composite...systems consisting of Ti/SiC, and H2O-filled porous ceramic media, by maximizing the relative acoustic bandgap for these media. The term acoustic here...stress minimization, global optimization, phonon bandgap, genetic algorithm, periodic elastic media, inhomogeneity, inclusion, porous media, acoustic

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Consistent and reproducible positioning in longitudinal imaging for phenotyping genetically modified swine

NASA Astrophysics Data System (ADS)

Hammond, Emily; Dilger, Samantha K. N.; Stoyles, Nicholas; Judisch, Alexandra; Morgan, John; Sieren, Jessica C.

2015-03-01

Recent growth of genetic disease models in swine has presented the opportunity to advance translation of developed imaging protocols, while characterizing the genotype to phenotype relationship. Repeated imaging with multiple clinical modalities provides non-invasive detection, diagnosis, and monitoring of disease to accomplish these goals; however, longitudinal scanning requires repeatable and reproducible positioning of the animals. A modular positioning unit was designed to provide a fixed, stable base for the anesthetized animal through transit and imaging. Post ventilation and sedation, animals were placed supine in the unit and monitored for consistent vitals. Comprehensive imaging was performed with a computed tomography (CT) chest-abdomen-pelvis scan at each screening time point. Longitudinal images were rigidly registered, accounting for rotation, translation, and anisotropic scaling, and the skeleton was isolated using a basic thresholding algorithm. Assessment of alignment was quantified via eleven pairs of corresponding points on the skeleton with the first time point as the reference. Results were obtained with five animals over five screening time points. The developed unit aided in skeletal alignment within an average of 13.13 +/- 6.7 mm for all five subjects providing a strong foundation for developing qualitative and quantitative methods of disease tracking.

The Soil Moisture Active Passive Mission (SMAP) Science Data Products: Results of Testing with Field Experiment and Algorithm Testbed Simulation Environment Data

NASA Technical Reports Server (NTRS)

Entekhabi, Dara; Njoku, Eni E.; O'Neill, Peggy E.; Kellogg, Kent H.; Entin, Jared K.

2010-01-01

Talk outline 1. Derivation of SMAP basic and applied science requirements from the NRC Earth Science Decadal Survey applications 2. Data products and latencies 3. Algorithm highlights 4. SMAP Algorithm Testbed 5. SMAP Working Groups and community engagement

Can the BMS Algorithm Decode Up to \\lfloor \\frac{d_G-g-1}{2}\\rfloor Errors? Yes, but with Some Additional Remarks

NASA Astrophysics Data System (ADS)

Sakata, Shojiro; Fujisawa, Masaya

It is a well-known fact [7], [9] that the BMS algorithm with majority voting can decode up to half the Feng-Rao designed distance dFR. Since dFR is not smaller than the Goppa designed distance dG, that algorithm can correct up to \\lfloor \\frac{d_G-1}{2}\\rfloor errors. On the other hand, it has been considered to be evident that the original BMS algorithm (without voting) [1], [2] can correct up to \\lfloor \\frac{d_G-g-1}{2}\\rfloor errors similarly to the basic algorithm by Skorobogatov-Vladut. But, is it true? In this short paper, we show that it is true, although we need a few remarks and some additional procedures for determining the Groebner basis of the error locator ideal exactly. In fact, as the basic algorithm gives a set of polynomials whose zero set contains the error locators as a subset, it cannot always give the exact error locators, unless the syndrome equation is solved to find the error values in addition.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

PubMed

Li, Yuhong; Gong, Guanghong; Li, Ni

2018-01-01

In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

Algorithmic formulation of control problems in manipulation

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1975-01-01

The basic characteristics of manipulator control algorithms are discussed. The state of the art in the development of manipulator control algorithms is briefly reviewed. Different end-point control techniques are described together with control algorithms which operate on external sensor (imaging, proximity, tactile, and torque/force) signals in realtime. Manipulator control development at JPL is briefly described and illustrated with several figures. The JPL work pays special attention to the front or operator input end of the control algorithms.

Uni10: an open-source library for tensor network algorithms

NASA Astrophysics Data System (ADS)

Kao, Ying-Jer; Hsieh, Yun-Da; Chen, Pochung

2015-09-01

We present an object-oriented open-source library for developing tensor network algorithms written in C++ called Uni10. With Uni10, users can build a symmetric tensor from a collection of bonds, while the bonds are constructed from a list of quantum numbers associated with different quantum states. It is easy to label and permute the indices of the tensors and access a block associated with a particular quantum number. Furthermore a network class is used to describe arbitrary tensor network structure and to perform network contractions efficiently. We give an overview of the basic structure of the library and the hierarchy of the classes. We present examples of the construction of a spin-1 Heisenberg Hamiltonian and the implementation of the tensor renormalization group algorithm to illustrate the basic usage of the library. The library described here is particularly well suited to explore and fast prototype novel tensor network algorithms and to implement highly efficient codes for existing algorithms.

Unraveling the genetic structure of Brazilian commercial sugarcane cultivars through microsatellite markers

PubMed Central

Manechini, João Ricardo Vieira; da Costa, Juliana Borges; Pereira, Bruna Turcatto; Carlini-Garcia, Luciana Aparecida; Xavier, Mauro Alexandre; Landell, Marcos Guimarães de Andrade

2018-01-01

The Brazilian sugarcane industry plays an important role in the worldwide supply of sugar and ethanol. Investigation into the genetic structure of current commercial cultivars and comparisons to the main ancestor species allow sugarcane breeding programs to better manage crosses and germplasm banks as well as to promote its rational use. In the present study, the genetic structure of a group of Brazilian cultivars currently grown by commercial producers was assessed through microsatellite markers and contrasted with a group of basic germplasm mainly composed of Saccharum officinarum and S. spontaneum accessions. A total of 285 alleles was obtained by a set of 12 SSRs primer pairs that taken together were able to efficiently distinguish and capture the genetic variability of sugarcane commercial cultivars and basic germplasm accessions allowing its application in a fast and cost-effective way for routine cultivar identification and management of sugarcane germplasm banks. Allelic distribution revealed that 97.6% of the cultivar alleles were found in the basic germplasm while 42% of the basic germplasm alleles were absent in cultivars. Of the absent alleles, 3% was exclusive to S. officinarum, 33% to S. spontaneum and 19% to other species/exotic hybrids. We found strong genetic differentiation between the Brazilian commercial cultivars and the two main species (S. officinarum: Φ^ST = 0.211 and S. spontaneum: Φ^ST = 0.216, P<0.001), and significant contribution of the latter in the genetic variability of commercial cultivars. Average dissimilarity within cultivars was 1.2 and 1.4 times lower than that within S. officinarum and S. spontaneum. Genetic divergence found between cultivars and S. spontaneum accessions has practical applications for energy cane breeding programs as the choice of more divergent parents will maximize the frequency of transgressive individuals in the progeny. PMID:29684082

Cleft Palate-Craniofacial Journal 50th anniversary editorial board commentary: anatomy, basic sciences, and genetics--then and now.

PubMed

Mooney, Mark P; Cooper, Gregory M; Marazita, Mary L

2014-05-01

To celebrate the 50th year of the Cleft Palate-Craniofacial Journal we look back to where we started in 1964 and where we are now, and we speculate about directions for the future in a "Then and Now" editorial series. This editorial examines changing trends and perspectives in anatomical, basic science, and genetic studies published in this 50-year interval. In volume 1 there were 45 total papers, seven (16%) of which were peer-reviewed basic science and genetic articles published: four in anatomy, three in craniofacial biology, and none in genetics. In contrast, in volume 50, of 113 articles there were 47 (42%) peer-reviewed basic science and genetic articles published: 30 in anatomy, five in craniofacial biology, and 12 in genetics. Topical analysis of published manuscripts then and now reveal that similar topics in anatomy and craniofacial biology are still being researched today (e.g., phenotypic variability, optimal timing of surgery, presurgical orthopedics, bone grafting); whereas, most of the more recent papers use advanced technology to address old questions. In contrast, genetic publications have clearly increased in frequency during the last 50 years, which parallels advances in the field during this time. However, all of us have noticed that the more "cutting-edge" papers in these areas are not being submitted for publication to the journal, but instead to discipline-specific journals. Concerted efforts are therefore indicated to attract and publish these cutting-edge papers in order to keep the Cleft Palate-Craniofacial Journal in the forefront of orofacial cleft and craniofacial anomaly research and to provide a valuable service to American Cleft Palate-Craniofacial Association members.

Unraveling the genetic structure of Brazilian commercial sugarcane cultivars through microsatellite markers.

PubMed

Manechini, João Ricardo Vieira; da Costa, Juliana Borges; Pereira, Bruna Turcatto; Carlini-Garcia, Luciana Aparecida; Xavier, Mauro Alexandre; Landell, Marcos Guimarães de Andrade; Pinto, Luciana Rossini

2018-01-01

The Brazilian sugarcane industry plays an important role in the worldwide supply of sugar and ethanol. Investigation into the genetic structure of current commercial cultivars and comparisons to the main ancestor species allow sugarcane breeding programs to better manage crosses and germplasm banks as well as to promote its rational use. In the present study, the genetic structure of a group of Brazilian cultivars currently grown by commercial producers was assessed through microsatellite markers and contrasted with a group of basic germplasm mainly composed of Saccharum officinarum and S. spontaneum accessions. A total of 285 alleles was obtained by a set of 12 SSRs primer pairs that taken together were able to efficiently distinguish and capture the genetic variability of sugarcane commercial cultivars and basic germplasm accessions allowing its application in a fast and cost-effective way for routine cultivar identification and management of sugarcane germplasm banks. Allelic distribution revealed that 97.6% of the cultivar alleles were found in the basic germplasm while 42% of the basic germplasm alleles were absent in cultivars. Of the absent alleles, 3% was exclusive to S. officinarum, 33% to S. spontaneum and 19% to other species/exotic hybrids. We found strong genetic differentiation between the Brazilian commercial cultivars and the two main species (S. officinarum: [Formula: see text] = 0.211 and S. spontaneum: [Formula: see text] = 0.216, P<0.001), and significant contribution of the latter in the genetic variability of commercial cultivars. Average dissimilarity within cultivars was 1.2 and 1.4 times lower than that within S. officinarum and S. spontaneum. Genetic divergence found between cultivars and S. spontaneum accessions has practical applications for energy cane breeding programs as the choice of more divergent parents will maximize the frequency of transgressive individuals in the progeny.

Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

PubMed

Gao, Bin; Li, Xiaoqing; Woo, Wai Lok; Tian, Gui Yun

2018-05-01

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

How Crossover Speeds up Building Block Assembly in Genetic Algorithms.

PubMed

Sudholt, Dirk

2017-01-01

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every ([Formula: see text]+[Formula: see text]) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate [Formula: see text] and [Formula: see text]. Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from [Formula: see text] to [Formula: see text]. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

High performance genetic algorithm for VLSI circuit partitioning

NASA Astrophysics Data System (ADS)

Dinu, Simona

2016-12-01

Partitioning is one of the biggest challenges in computer-aided design for VLSI circuits (very large-scale integrated circuits). This work address the min-cut balanced circuit partitioning problem- dividing the graph that models the circuit into almost equal sized k sub-graphs while minimizing the number of edges cut i.e. minimizing the number of edges connecting the sub-graphs. The problem may be formulated as a combinatorial optimization problem. Experimental studies in the literature have shown the problem to be NP-hard and thus it is important to design an efficient heuristic algorithm to solve it. The approach proposed in this study is a parallel implementation of a genetic algorithm, namely an island model. The information exchange between the evolving subpopulations is modeled using a fuzzy controller, which determines an optimal balance between exploration and exploitation of the solution space. The results of simulations show that the proposed algorithm outperforms the standard sequential genetic algorithm both in terms of solution quality and convergence speed. As a direction for future study, this research can be further extended to incorporate local search operators which should include problem-specific knowledge. In addition, the adaptive configuration of mutation and crossover rates is another guidance for future research.

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.

Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm

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

Quang Binh, DO; Gyuhong, ROH; Hangbok, CHOI

2006-07-01

This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and the proposed method comply with the key features of the refueling strategy of the CANDU reactor which adopts an on-power refueling operation. In this study, a genetic algorithm combined with an elitism strategy was used to automatically search for themore » refueling patterns. The objective of the optimization was to maximize the discharge burn-up of the refueling bundles, minimize the maximum channel power, or minimize the maximum change in the zone controller unit (ZCU) water levels. A combination of these objectives was also investigated. The constraints include the discharge burn-up, maximum channel power, maximum bundle power, channel power peaking factor and the ZCU water level. A refueling pattern that represents the refueling rate and channels was coded by a one-dimensional binary chromosome, which is a string of binary numbers 0 and 1. A computer program was developed in FORTRAN 90 running on an HP 9000 workstation to conduct the search for the optimal refueling patterns for a CANDU reactor at the equilibrium state. The results showed that it was possible to apply genetic algorithms to automatically search for the refueling channels of the CANDU reactor. The optimal refueling patterns were compared with the solutions obtained from the AUTOREFUEL program and the results were consistent with each other. (authors)« less