Genetic algorithms as discovery programs
Hilliard, M.R.; Liepins, G.
1986-01-01
Genetic algorithms are mathematical counterparts to natural selection and gene recombination. As such, they have provided one of the few significant breakthroughs in machine learning. Used with appropriate reward functions and apportionment of credit, they have been successfully applied to gas pipeline operation, x-ray registration and mathematical optimization problems. This paper discusses the basics of genetic algorithms, describes a few successes, and reports on current progress at Oak Ridge National Laboratory in applications to set covering and simulated robots.
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.
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
EVOLVING RETRIEVAL ALGORITHMS WITH A GENETIC PROGRAMMING SCHEME
J. THEILER; ET AL
1999-06-01
The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or rejected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this ''forward'' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to ''evolve'' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated ''ground truth;'' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.
Evolving retrieval algorithms with a genetic programming scheme
NASA Astrophysics Data System (ADS)
Theiler, James P.; Harvey, Neal R.; Brumby, Steven P.; Szymanski, John J.; Alferink, Steve; Perkins, Simon J.; Porter, Reid B.; Bloch, Jeffrey J.
1999-10-01
The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or reflected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this 'forward' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to 'evolve' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated 'ground truth;' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.
Empirical study of self-configuring genetic programming algorithm performance and behaviour
NASA Astrophysics Data System (ADS)
Semenkin, E.; Semenkina, M.
2015-01-01
The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems.
Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi; Mousavi, Reza
2015-01-01
In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy. PMID:25786796
NASA Astrophysics Data System (ADS)
Theofilatos, Konstantinos; Georgopoulos, Efstratios; Likothanassis, Spiridon
2009-09-01
In this paper, a variation of traditional Genetic Programming(GP) is used to model the MagnetoencephaloGram(MEG) of Epileptic Patients. This variation is Linear Genetic Programming(LGP). LGP is a particular subset of GP wherein computer programs in population are represented as a sequence of instructions from imperative programming language or machine language. The derived models from this method were simplified using genetic algorithms. The proposed method was used to model the MEG signal of epileptic patients using 6 different datasets. Each dataset uses different number of previous values of MEG to predict the next value. The models were tested in datasets different from the ones which were used to produce them and the results were very promising.
NASA Astrophysics Data System (ADS)
Sastry, Kumara Narasimha
2007-03-01
Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common
NASA Astrophysics Data System (ADS)
Biswas, Papun; Chakraborti, Debjani
2010-10-01
This paper describes how the genetic algorithms (GAs) can be efficiently used to fuzzy goal programming (FGP) formulation of optimal power flow problems having multiple objectives. In the proposed approach, the different constraints, various relationships of optimal power flow calculations are fuzzily described. In the model formulation of the problem, the membership functions of the defined fuzzy goals are characterized first for measuring the degree of achievement of the aspiration levels of the goals specified in the decision making context. Then, the achievement function for minimizing the regret for under-deviations from the highest membership value (unity) of the defined membership goals to the extent possible on the basis of priorities is constructed for optimal power flow problems. In the solution process, the GA method is employed to the FGP formulation of the problem for achievement of the highest membership value (unity) of the defined membership functions to the extent possible in the decision making environment. In the GA based solution search process, the conventional Roulette wheel selection scheme, arithmetic crossover and random mutation are taken into consideration to reach a satisfactory decision. The developed method has been tested on IEEE 6-generator 30-bus System. Numerical results show that this method is promising for handling uncertain constraints in practical power systems.
General cardinality genetic algorithms
Koehler; Bhattacharyya; Vose
1997-01-01
A complete generalization of the Vose genetic algorithm model from the binary to higher cardinality case is provided. Boolean AND and EXCLUSIVE-OR operators are replaced by multiplication and addition over rings of integers. Walsh matrices are generalized with finite Fourier transforms for higher cardinality usage. Comparison of results to the binary case are provided. PMID:10021767
Genetic algorithms and MCML program for recovery of optical properties of homogeneous turbid media
Morales Cruzado, Beatriz; y Montiel, Sergio Vázquez; Atencio, José Alberto Delgado
2013-01-01
In this paper, we present and validate a new method for optical properties recovery of turbid media with slab geometry. This method is an iterative method that compares diffuse reflectance and transmittance, measured using integrating spheres, with those obtained using the known algorithm MCML. The search procedure is based in the evolution of a population due to selection of the best individual, i.e., using a genetic algorithm. This new method includes several corrections such as non-linear effects in integrating spheres measurements and loss of light due to the finite size of the sample. As a potential application and proof-of-principle experiment of this new method, we use this new algorithm in the recovery of optical properties of blood samples at different degrees of coagulation. PMID:23504404
Using Dynamic Programming and Genetic Algorithms to Reduce Erosion Risks From Forest Roads
NASA Astrophysics Data System (ADS)
Madej, M.; Eschenbach, E.; Teasley, R.; Diaz, C.; Wartella, J.; Simi, J.
2002-12-01
Many anadromous fisheries streams in the Pacific Northwest have been damaged by various land use activities, including timber harvest and road construction. Unpaved forest roads can cause erosion and downstream sedimentation damage in anadromous fish-bearing streams. Although road decommissioning and road upgrading activities have been conducted on many of these roads, these activities have usually been implemented and evaluated on a site-specific basis without the benefit of a watershed perspective. Land managers still struggle with designing the most effective road treatment plan to minimize erosion while keeping costs reasonable across a large land base. Trade-offs between costs of different levels of treatment and the net effect on reducing sediment risks to streams need to be quantified. For example, which problems should be treated first, and by what treatment method? Is it better to fix one large problem or 100 small problems? If sediment reduction to anadromous fish-bearing streams is the desired outcome of road treatment activities, a more rigorous evaluation of risks and optimization of treatments is needed. Two approaches, Dynamic Programming (DP) and Genetic Algorithms (GA), were successfully used to determine the most effective treatment levels for roads and stream crossings in a pilot study basin with approximately 200 road segments and stream crossings and in an actual watershed with approximately 600 road segments and crossings. The optimization models determine the treatment levels for roads and crossings that maximize the total sediment saved within a watershed while maintaining the total treatment cost within the specified budget. The optimization models import GIS data on roads and crossings and export the optimal treatment level for each road and crossing to the GIS watershed model.
NASA Astrophysics Data System (ADS)
Gladwin, D.; Stewart, P.; Stewart, J.
2011-02-01
This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control
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.
Constraints in Genetic Programming
NASA Technical Reports Server (NTRS)
Janikow, Cezary Z.
1996-01-01
Genetic programming refers to a class of genetic algorithms utilizing generic representation in the form of program trees. For a particular application, one needs to provide the set of functions, whose compositions determine the space of program structures being evolved, and the set of terminals, which determine the space of specific instances of those programs. The algorithm searches the space for the best program for a given problem, applying evolutionary mechanisms borrowed from nature. Genetic algorithms have shown great capabilities in approximately solving optimization problems which could not be approximated or solved with other methods. Genetic programming extends their capabilities to deal with a broader variety of problems. However, it also extends the size of the search space, which often becomes too large to be effectively searched even by evolutionary methods. Therefore, our objective is to utilize problem constraints, if such can be identified, to restrict this space. In this publication, we propose a generic constraint specification language, powerful enough for a broad class of problem constraints. This language has two elements -- one reduces only the number of program instances, the other reduces both the space of program structures as well as their instances. With this language, we define the minimal set of complete constraints, and a set of operators guaranteeing offspring validity from valid parents. We also show that these operators are not less efficient than the standard genetic programming operators if one preprocesses the constraints - the necessary mechanisms are identified.
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
NASA Astrophysics Data System (ADS)
Qiu, J. P.; Niu, D. X.
Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.
NASA Astrophysics Data System (ADS)
Chen, Zheng; Mi, Chris Chunting; Xiong, Rui; Xu, Jun; You, Chenwen
2014-02-01
This paper introduces an online and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on analytic analysis between fuel-rate and battery current at different driveline power and vehicle speed, quadratic equations are applied to simulate the relationship between battery current and vehicle fuel-rate. The power threshold at which engine is turned on is optimized by genetic algorithm (GA) based on vehicle fuel-rate, battery state of charge (SOC) and driveline power demand. The optimal battery current when the engine is on is calculated using quadratic programming (QP) method. The proposed algorithm can control the battery current effectively, which makes the engine work more efficiently and thus reduce the fuel-consumption. Moreover, the controller is still applicable when the battery is unhealthy. Numerical simulations validated the feasibility of the proposed controller.
NASA Astrophysics Data System (ADS)
Paino, A.; Keller, J.; Popescu, M.; Stone, K.
2014-06-01
In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.
Scheduling with genetic algorithms
NASA Technical Reports Server (NTRS)
Fennel, Theron R.; Underbrink, A. J., Jr.; Williams, George P. W., Jr.
1994-01-01
In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.
Messy genetic algorithms: Recent developments
Kargupta, H.
1996-09-01
Messy genetic algorithms define a rare class of algorithms that realize the need for detecting appropriate relations among members of the search domain in optimization. This paper reviews earlier works in messy genetic algorithms and describes some recent developments. It also describes the gene expression messy GA (GEMGA)--an {Omicron}({Lambda}{sup {kappa}}({ell}{sup 2} + {kappa})) sample complexity algorithm for the class of order-{kappa} delineable problems (problems that can be solved by considering no higher than order-{kappa} relations) of size {ell} and alphabet size {Lambda}. Experimental results are presented to demonstrate the scalability of the GEMGA.
Scheduling Jobs with Genetic Algorithms
NASA Astrophysics Data System (ADS)
Ferrolho, António; Crisóstomo, Manuel
Most scheduling problems are NP-hard, the time required to solve the problem optimally increases exponentially with the size of the problem. Scheduling problems have important applications, and a number of heuristic algorithms have been proposed to determine relatively good solutions in polynomial time. Recently, genetic algorithms (GA) are successfully used to solve scheduling problems, as shown by the growing numbers of papers. GA are known as one of the most efficient algorithms for solving scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.
NASA Astrophysics Data System (ADS)
Huang, Wen-Cheng; Yuan, Lun-Chin; Lee, Chi-Ming
2002-12-01
The objective of this paper is to present a genetic algorithm-based stochastic dynamic programming (GA-based SDP) to cope with the dimensionality problem of a multiple-reservoir system. The joint long-term operation of a parallel reservoir system in the Feitsui and Shihmen reservoirs in northern Taiwan demonstrates the successful application of the proposed GA-based SDP model. Within the case study system it is believed that GA is a useful technique in supporting optimization. Though the employment of GA-based SDP may be time consuming as it proceeds through generation by generation, the model can overcome the "dimensionality curse" in searching solutions. Simulation results show Feitsui's surplus water can be utilized efficiently to fill Shihmen's deficit water without affecting Feitsui's main purpose as Taipei city's water supply. The optimal joint operation suggests that Feitsui, on average, can provide 650,000 m3/day and 920,000 m3/day to Shihmen during the wet season and dry season, respectively.
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.
Simultaneous stabilization using genetic algorithms
Benson, R.W.; Schmitendorf, W.E. . Dept. of Mechanical Engineering)
1991-01-01
This paper considers the problem of simultaneously stabilizing a set of plants using full state feedback. The problem is converted to a simple optimization problem which is solved by a genetic algorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.
Programming parallel vision algorithms
Shapiro, L.G.
1988-01-01
Computer vision requires the processing of large volumes of data and requires parallel architectures and algorithms to be useful in real-time, industrial applications. The INSIGHT dataflow language was designed to allow encoding of vision algorithms at all levels of the computer vision paradigm. INSIGHT programs, which are relational in nature, can be translated into a graph structure that represents an architecture for solving a particular vision problem or a configuration of a reconfigurable computational network. The authors consider here INSIGHT programs that produce a parallel net architecture for solving low-, mid-, and high-level vision tasks.
Problem solving with genetic algorithms and Splicer
NASA Technical Reports Server (NTRS)
Bayer, Steven E.; Wang, Lui
1991-01-01
Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
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.
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…
Excursion-Set-Mediated Genetic Algorithm
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.
Genetic Algorithms with Local Minimum Escaping Technique
NASA Astrophysics Data System (ADS)
Tamura, Hiroki; Sakata, Kenichiro; Tang, Zheng; Ishii, Masahiro
In this paper, we propose a genetic algorithm(GA) with local minimum escaping technique. This proposed method uses the local minimum escaping techique. It can escape from the local minimum by correcting parameters when genetic algorithm falls into a local minimum. Simulations are performed to scheduling problem without buffer capacity using this proposed method, and its validity is shown.
Cao, Buwen; Luo, Jiawei; Liang, Cheng; Wang, Shulin; Song, Dan
2015-10-01
The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions. PMID:26298638
Saving Resources with Plagues in Genetic Algorithms
de Vega, F F; Cantu-Paz, E; Lopez, J I; Manzano, T
2004-06-15
The population size of genetic algorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in practice the sizing of populations is still usually performed by trial and error. These trials might lead to find a population that is large enough to reach a satisfactory solution, but there may still be opportunities to optimize the computational cost by reducing the size of the population. This paper presents a technique called plague that periodically removes a number of individuals from the population as the GA executes. Recently, the usefulness of the plague has been demonstrated for genetic programming. The objective of this paper is to extend the study of plagues to genetic algorithms. We experiment with deceptive trap functions, a tunable difficult problem for GAs, and the experiments show that plagues can save computational time while maintaining solution quality and reliability.
Fuzzy Information Retrieval Using Genetic Algorithms and Relevance Feedback.
ERIC Educational Resources Information Center
Petry, Frederick E.; And Others
1993-01-01
Describes an approach that combines concepts from information retrieval, fuzzy set theory, and genetic programing to improve weighted Boolean query formulation via relevance feedback. Highlights include background on information retrieval systems; genetic algorithms; subproblem formulation; and preliminary results based on a testbed. (Contains 12…
Genetic-Algorithm Tool For Search And Optimization
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven
1995-01-01
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
NASA Astrophysics Data System (ADS)
Hsu, Chih-Ming
2014-12-01
Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.
Genetic-algorithm-based tri-state neural networks
NASA Astrophysics Data System (ADS)
Uang, Chii-Maw; Chen, Wen-Gong; Horng, Ji-Bin
2002-09-01
A new method, using genetic algorithms, for constructing a tri-state neural network is presented. The global searching features of the genetic algorithms are adopted to help us easily find the interconnection weight matrix of a bipolar neural network. The construction method is based on the biological nervous systems, which evolve the parameters encoded in genes. Taking the advantages of conventional (binary) genetic algorithms, a two-level chromosome structure is proposed for training the tri-state neural network. A Matlab program is developed for simulating the network performances. The results show that the proposed genetic algorithms method not only has the features of accurate of constructing the interconnection weight matrix, but also has better network performance.
Genetic algorithms at UC Davis/LLNL
Vemuri, V.R.
1993-12-31
A tutorial introduction to genetic algorithms is given. This brief tutorial should serve the purpose of introducing the subject to the novice. The tutorial is followed by a brief commentary on the term project reports that follow.
Genetic algorithms and supernovae type Ia analysis
Bogdanos, Charalampos; Nesseris, Savvas E-mail: nesseris@nbi.dk
2009-05-15
We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state w(z) {identical_to} P{sub DE}/{rho}{sub DE}. Specifically, we will give a brief introduction to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SNIa data. As a non-parametric method, genetic algorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.
Self-adaptive parameters in genetic algorithms
NASA Astrophysics Data System (ADS)
Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain
2004-04-01
Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a genetic algorithm"s parameters can be encoded within the chromosome of each individual. The parameters" values are entirely dependent on the evolution mechanism and on the problem context. Our preliminary results show that a GA is able to learn and evaluate the quality of self-set parameters according to their degree of contribution to the resolution of the problem. These results are indicative of a promising approach to the development of GAs with self-adaptive parameter settings that do not require the user to pre-adjust parameters at the outset.
Adaptive sensor fusion using genetic algorithms
Fitzgerald, D.S.; Adams, D.G.
1994-08-01
Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ``fuzzy`` sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion.
Genetic algorithm dose minimization for an operational layout.
McLawhorn, S. L.; Kornreich, D. E.; Dudziak, Donald J.
2002-01-01
In an effort to reduce the dose to operating technicians performing fixed-time procedures on encapsulated source material, a program has been developed to optimize the layout of workstations within a facility by use of a genetic algorithm. Taking into account the sources present at each station and the time required to complete each procedure, the program utilizes a point kernel dose calculation tool for dose estimates. The genetic algorithm driver employs the dose calculation code as a cost function to determine the optimal spatial arrangement of workstations to minimize the total worker dose.
Reactive power optimization by genetic algorithm
Iba, Kenji )
1994-05-01
This paper presents a new approach to optimal reactive power planning based on a genetic algorithm. Many outstanding methods to this problem have been proposed in the past. However, most of these approaches have the common defect of being caught to a local minimum solution. The integer problem which yields integer value solutions for discrete controllers/banks still remains as a difficult one. The genetic algorithm is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using multiple paths and treat integer problems naturally. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities. Although this method is not as fast as sophisticated traditional methods, the concept is quite promising and useful.
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 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.
Algorithmic advances in stochastic programming
Morton, D.P.
1993-07-01
Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decomposition-based stochastic programming algorithms. The first type of algorithm addresses problems with a ``manageable`` number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ``real-world`` multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decomposition-based algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two sampling-based algorithms are considered, and preliminary empirical coverage results are presented.
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.
Research on Routing Selection Algorithm Based on Genetic Algorithm
NASA Astrophysics Data System (ADS)
Gao, Guohong; Zhang, Baojian; Li, Xueyong; Lv, Jinna
The hereditary algorithm is a kind of random searching and method of optimizing based on living beings natural selection and hereditary mechanism. In recent years, because of the potentiality in solving complicate problems and the successful application in the fields of industrial project, hereditary algorithm has been widely concerned by the domestic and international scholar. Routing Selection communication has been defined a standard communication model of IP version 6.This paper proposes a service model of Routing Selection communication, and designs and implements a new Routing Selection algorithm based on genetic algorithm.The experimental simulation results show that this algorithm can get more resolution at less time and more balanced network load, which enhances search ratio and the availability of network resource, and improves the quality of service.
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.
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.
Genetic algorithms: What computers can learn from Darwin
Walbridge, C.T. )
1989-01-01
In this article the author posits a field of computing based on the genetic algorithm. This approach to programming mimics evolution by utilizing a computer to solve problems on a trial and error basis and ascertain the best answer through natural selection of the best of the computer's guesses. The author discusses the viability of this system in comparison to that of artificial intelligence.
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.
Robot path planning using a genetic algorithm
NASA Technical Reports Server (NTRS)
Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu
1988-01-01
Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.
Equilibrium stellar systems with genetic algorithms
NASA Astrophysics Data System (ADS)
Gularte, E.; Carpintero, D. D.
In 1979, M Schwarzschild showed that it is possible to build an equilibrium triaxial stellar system. However, the linear programmation used to that goal was not able to determine the uniqueness of the solution, nor even if that solution was the optimum one. Genetic algorithms are ideal tools to find a solution to this problem. In this work, we use a genetic algorithm to reproduce an equilibrium spherical stellar system from a suitable set of predefined orbits, obtaining the best solution attainable with the provided set. FULL TEXT IN SPANISH
Genetic Algorithms for Digital Quantum Simulations
NASA Astrophysics Data System (ADS)
Las Heras, U.; Alvarez-Rodriguez, U.; Solano, E.; Sanz, M.
2016-06-01
We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.
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.
Facial Composite System Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Zahradníková, Barbora; Duchovičová, Soňa; Schreiber, Peter
2014-12-01
The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.
The Applications of Genetic Algorithms in Medicine.
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.]. PMID:26676060
The Applications of Genetic Algorithms in Medicine
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
Predicting complex mineral structures using genetic algorithms.
Mohn, Chris E; Kob, Walter
2015-10-28
We show that symmetry-adapted genetic algorithms are capable of finding the ground state of a range of complex crystalline phases including layered- and incommensurate super-structures. This opens the way for the atomistic prediction of complex crystal structures of functional materials and mineral phases. PMID:26441052
MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
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...
Multiobjective Genetic Algorithm applied to dengue control.
Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F
2014-12-01
Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. PMID:25230238
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.
Fault detection using genetic programming
NASA Astrophysics Data System (ADS)
Zhang, Liang; B. Jack, Lindsay; Nandi, Asoke K.
2005-03-01
Genetic programming (GP) is a stochastic process for automatically generating computer programs. GP has been applied to a variety of problems which are too wide to reasonably enumerate. As far as the authors are aware, it has rarely been used in condition monitoring (CM). In this paper, GP is used to detect faults in rotating machinery. Featuresets from two different machines are used to examine the performance of two-class normal/fault recognition. The results are compared with a few other methods for fault detection: Artificial neural networks (ANNs) have been used in this field for many years, while support vector machines (SVMs) also offer successful solutions. For ANNs and SVMs, genetic algorithms have been used to do feature selection, which is an inherent function of GP. In all cases, the GP demonstrates performance which equals or betters that of the previous best performing approaches on these data sets. The training times are also found to be considerably shorter than the other approaches, whilst the generated classification rules are easy to understand and independently validate.
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.
Production scheduling and rescheduling with genetic algorithms.
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. PMID:10199993
Application of Genetic Algorithms in Seismic Tomography
NASA Astrophysics Data System (ADS)
Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos
2010-05-01
In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the
Genetic algorithms for minimal source reconstructions
Lewis, P.S.; Mosher, J.C.
1993-12-01
Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.
The genetic algorithms for trajectory optimization
NASA Astrophysics Data System (ADS)
Janin, G.; Gomez-Tierno, M. A.
1985-10-01
Possible difficulties encountered when solving space flight trajectory optimization problems are recalled. The need of a global optimization scheme is realized. Nondeterministic methods, called here stochastic methods, seem to be good candidates for solving these types of problems. A particular class of such methods, modelled upon search strategies employed in natural adaptation, is proposed here: the genetic algorithms. Two models, the mutation-selection and the crossover-selection, are discussed and remarks resulting from applications to test problems and space flight problems are made. It is concluded that a considerable effort is still needed for developing efficient schemes using genetic algorithms. However, they appear to offer an entirely original way for solving a large class of global optimization problems and they are particularly well-suited for parallel processing to be used in the fifth generation computers.
Fashion sketch design by interactive genetic algorithms
NASA Astrophysics Data System (ADS)
Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.
2012-11-01
Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.
PSS Parameters Tuning Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Abdulrahim, M.; Almoula, Zakaria Fadl; Al-Hafid, Hafid
2008-10-01
Optimal tuning of power system stabilizer (PSS) parameters using genetic algorithm with single objective function is presented in this paper. A Single Machine Infinite Bus (SMIB) system is considered. The main objective of this research paper is to investigate the suitability of genetic algorithm for effective tuning of parameters of the power system stabilizer in a single machine infinite bus system. A conventional speed based lead-lag PSS is used. A simple and effective method of tuning the parameters of PSS is proposed which is posed as an optimization formulation by maximizing the damping of modes of oscillations of the SMIB system over a wide range of loading conditions and different system configurations. It is found that GA based PSS with single objective design shows improved dynamic performance over Conventional PSS over a wide range of operating conditions and different system parameters.
Efficient genetic algorithms using discretization scheduling.
McLay, Laura A; Goldberg, David E
2005-01-01
In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations use numerical methods with varying discretization. In these types of applications, the cost and accuracy vary from discretization errors when implicit or explicit quadrature is used to estimate the function evaluations. This paper examines discretization scheduling, or how to vary the discretization within the genetic algorithm in order to use the least amount of computation time for a solution of a desired quality. The effectiveness of discretization scheduling can be determined by comparing its computation time to the computation time of a GA using a constant discretization. There are three ingredients for the discretization scheduling: population sizing, estimated time for each function evaluation and predicted convergence time analysis. Idealized one- and two-dimensional experiments and an inverse groundwater application illustrate the computational savings to be achieved from using discretization scheduling. PMID:16156928
Allocating Railway Platforms Using A Genetic Algorithm
NASA Astrophysics Data System (ADS)
Clarke, M.; Hinde, C. J.; Withall, M. S.; Jackson, T. W.; Phillips, I. W.; Brown, S.; Watson, R.
This paper describes an approach to automating railway station platform allocation. The system uses a Genetic Algorithm (GA) to find how a station’s resources should be allocated. Real data is used which needs to be transformed to be suitable for the automated system. Successful or ‘fit’ allocations provide a solution that meets the needs of the station schedule including platform re-occupation and various other constraints. The system associates the train data to derive the station requirements. The Genetic Algorithm is used to derive platform allocations. Finally, the system may be extended to take into account how further parameters that are external to the station have an effect on how an allocation should be applied. The system successfully allocates around 1000 trains to platforms in around 30 seconds requiring a genome of around 1000 genes to achieve this.
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Medical image segmentation using genetic algorithms.
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. PMID:19272859
Genetic Algorithms for Digital Quantum Simulations.
Las Heras, U; Alvarez-Rodriguez, U; Solano, E; Sanz, M
2016-06-10
We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors. PMID:27341220
Predicting mining activity with parallel genetic algorithms
Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.
2005-01-01
We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.
Modeling a magnetostrictive transducer using genetic algorithm
NASA Astrophysics Data System (ADS)
Almeida, L. A. L.; Deep, G. S.; Lima, A. M. N.; Neff, H.
2001-05-01
This work reports on the applicability of the genetic algorithm (GA) to the problem of parameter determination of magnetostrictive transducers. A combination of the Jiles-Atherton hysteresis model with a quadratic moment rotation model is simulated using known parameters of a sensor. The simulated sensor data are then used as input data for the GA parameter calculation method. Taking the previously known parameters, the accuracy of the GA parameter calculation method can be evaluated.
Quantum-Inspired Genetic Algorithm or Quantum Genetic Algorithm: Which Is It?
NASA Astrophysics Data System (ADS)
Jones, Erika
2015-04-01
Our everyday work focuses on genetic algorithms (GAs) related to quantum computing where we call ``related'' algorithms those falling into one of two classes: (1) GAs run on classical computers but making use of quantum mechanical (QM) constructs and (2) GAs run on quantum hardware. Though convention has yet to be set with respect to usage of the accepted terms quantum-inspired genetic algorithm (QIGA) and quantum genetic algorithm (QGA), we find the two terms highly suitable respectively as labels for the aforementioned classes. With these specific definitions in mind, the difference between the QIGA and QGA is greater than might first be appreciated, particularly by those coming from a perspective emphasizing GA use as a general computational tool irrespective of QM aspects (1) suggested by QIGAs and (2) inherent in QGAs. We offer a theoretical standpoint highlighting key differences-both obvious, and more significantly, subtle-to be considered in general design of a QIGA versus that of a QGA.
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 optimization of atomic clusters
Morris, J.R.; Deaven, D.M.; Ho, K.M.; Wang, C.Z.; Pan, B.C.; Wacker, J.G.; Turner, D.E. |
1996-12-31
The authors have been using genetic algorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process.
Economic Dispatch Using Genetic Algorithm Based Hybrid Approach
Tahir Nadeem Malik; Aftab Ahmad; Shahab Khushnood
2006-07-01
Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)
Solving a multistage partial inspection problem using genetic algorithms
Heredia-Langner, Alejandro ); Montgomery, D C.; Carlyle, W M.
2002-01-01
Traditionally, the multistage inspection problem has been formulated as consisting of a decision schedule where some manufacturing stages receive full inspection and the rest none. Dynamic programming and heuristic methods (like local search) are the most commonly used solution techniques. A highly constrained multistage inspection problem is presented where all stages must receive partial rectifying inspection and it is solved using a real-valued genetic algorithm. This solution technique can handle multiple objectives and quality constraints effectively.
Enhanced algorithms for stochastic programming
Krishna, A.S.
1993-09-01
In this dissertation, we present some of the recent advances made in solving two-stage stochastic linear programming problems of large size and complexity. Decomposition and sampling are two fundamental components of techniques to solve stochastic optimization problems. We describe improvements to the current techniques in both these areas. We studied different ways of using importance sampling techniques in the context of Stochastic programming, by varying the choice of approximation functions used in this method. We have concluded that approximating the recourse function by a computationally inexpensive piecewise-linear function is highly efficient. This reduced the problem from finding the mean of a computationally expensive functions to finding that of a computationally inexpensive function. Then we implemented various variance reduction techniques to estimate the mean of a piecewise-linear function. This method achieved similar variance reductions in orders of magnitude less time than, when we directly applied variance-reduction techniques directly on the given problem. In solving a stochastic linear program, the expected value problem is usually solved before a stochastic solution and also to speed-up the algorithm by making use of the information obtained from the solution of the expected value problem. We have devised a new decomposition scheme to improve the convergence of this algorithm.
Using a genetic algorithm to solve fluid-flow problems
Pryor, R.J. )
1990-06-01
Genetic algorithms are based on the mechanics of the natural selection and natural genetics processes. These algorithms are finding increasing application to a wide variety of engineering optimization and machine learning problems. In this paper, the authors demonstrate the use of a genetic algorithm to solve fluid flow problems. Specifically, the authors use the algorithm to solve the one-dimensional flow equations for a pipe.
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.
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.
Fuzzy controller design by parallel genetic algorithms
NASA Astrophysics Data System (ADS)
Mondelli, G.; Castellano, G.; Attolico, Giovanni; Distante, Arcangelo
1998-03-01
Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. Genetic Algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.
Genetic algorithm for disassembly process planning
NASA Astrophysics Data System (ADS)
Kongar, Elif; Gupta, Surendra M.
2002-02-01
When a product reaches its end of life, there are several options available for processing it including reuse, remanufacturing, recycling, and disposing (the least desirable option). In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with the increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly process planning be developed. In this paper, we present a genetic algorithm for disassembly process planning. A case example is considered to demonstrate the functionality of the algorithm.
Dominant takeover regimes for genetic algorithms
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learning to natural genetic laws. The present work addresses the problem of obtaining the dominant takeover regimes in the GA dynamics. Estimated GA run times are computed for slow and fast convergence in the limits of high and low fitness ratios. Using Euler's device for obtaining partial sums in closed forms, the result relaxes the previously held requirements for long time limits. Analytical solution reveal that appropriately accelerated regimes can mark the ascendancy of the most fit solution. In virtually all cases, the weak (logarithmic) dependence of convergence time on problem size demonstrates the potential for the GA to solve large N-P complete problems.
Designing conducting polymers using genetic algorithms
NASA Astrophysics Data System (ADS)
Giro, R.; Cyrillo, M.; Galvão, D. S.
2002-11-01
We have developed a new methodology to design conducting polymers with pre-specified properties. The methodology is based on the use of genetic algorithms (GAs) coupled to Negative Factor Counting technique. We present the results for a case study of polyanilines, one of the most important families of conducting polymers. The methodology proved to be able of generating automatic solutions for the problem of determining the optimum relative concentration for binary and ternary disordered polyaniline alloys exhibiting metallic properties. The methodology is completely general and can be used to design new classes of materials.
Modeling of Nonlinear Systems using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Hayashi, Kayoko; Yamamoto, Toru; Kawada, Kazuo
In this paper, a newly modeling system by using Genetic Algorithm (GA) is proposed. The GA is an evolutionary computational method that simulates the mechanisms of heredity or evolution of living things, and it is utilized in optimization and in searching for optimized solutions. Most process systems have nonlinearities, so it is necessary to anticipate exactly such systems. However, it is difficult to make a suitable model for nonlinear systems, because most nonlinear systems have a complex structure. Therefore the newly proposed method of modeling for nonlinear systems uses GA. Then, according to the newly proposed scheme, the optimal structure and parameters of the nonlinear model are automatically generated.
Genetic algorithms for modelling and optimisation
NASA Astrophysics Data System (ADS)
McCall, John
2005-12-01
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.
Applications of Genetic Programming in Cancer Research
Worzel, William P.; Yu, Jianjun; Almal, Arpit A.; Chinnaiyan, Arul M.
2012-01-01
The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allows scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future. PMID:18929677
A genetic algorithm solution to the unit commitment problem
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 units and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.
National Dairy Genetic Evaluation Program
Technology Transfer Automated Retrieval System (TEKTRAN)
The National Dairy Genetic Evaluation Program is a continuation of ongoing USDA collaboration with the U.S. dairy industry on genetic evaluation of dairy cattle since 1908. Data are provided by dairy records processing centers (yield, health, pedigree, and reproduction traits), breed registry societ...
Adaptive sensor tasking using genetic algorithms
NASA Astrophysics Data System (ADS)
Shea, Peter J.; Kirk, Joe; Welchons, Dave
2007-04-01
Today's battlefield environment contains a large number of sensors, and sensor types, onboard multiple platforms. The set of sensor types includes SAR, EO/IR, GMTI, AMTI, HSI, MSI, and video, and for each sensor type there may be multiple sensing modalities to select from. In an attempt to maximize sensor performance, today's sensors employ either static tasking approaches or require an operator to manually change sensor tasking operations. In a highly dynamic environment this leads to a situation whereby the sensors become less effective as the sensing environments deviates from the assumed conditions. Through a Phase I SBIR effort we developed a system architecture and a common tasking approach for solving the sensor tasking problem for a multiple sensor mix. As part of our sensor tasking effort we developed a genetic algorithm based task scheduling approach and demonstrated the ability to automatically task and schedule sensors in an end-to-end closed loop simulation. Our approach allows for multiple sensors as well as system and sensor constraints. This provides a solid foundation for our future efforts including incorporation of other sensor types. This paper will describe our approach for scheduling using genetic algorithms to solve the sensor tasking problem in the presence of resource constraints and required task linkage. We will conclude with a discussion of results for a sample problem and of the path forward.
Instrument design and optimization using genetic algorithms
Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter
2006-10-15
This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.
Optimisation of nonlinear motion cueing algorithm based on genetic algorithm
NASA Astrophysics Data System (ADS)
Asadi, Houshyar; Mohamed, Shady; Rahim Zadeh, Delpak; Nahavandi, Saeid
2015-04-01
Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching
Implementation of genetic algorithm for distribution systems loss minimum re-configuration
Nara, K.; Shiose, A. ); Kitagawa, M.; Ishihara, T. )
1992-08-01
In this paper, a distribution systems loss minimum reconfiguration method by genetic algorithm is proposed. The problem is a complex mixed integer programming problem and is very difficult to solve by a mathematical programming approach. A genetic algorithm (GA) is a search or optimization algorithm based on the mechanics of natural selection and natural genetics. Since GA is suitable to solve combinatorial optimization problems, it can be successfully applied to problems of loss minimum in distribution systems. Numerical examples demonstrate the validity and effectiveness of the proposed methodology.
A genetic algorithm to reduce stream channel cross section data
Berenbrock, C.
2006-01-01
A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.
Multidisciplinary design optimization using genetic algorithms
NASA Astrophysics Data System (ADS)
Unal, Resit
1994-12-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared
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.
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
Inversion for seismic anisotropy using genetic algorithms
Horne, S. Univ. of Edinburgh . Dept. of Geology and Geophysics); MacBeth, C. . Dept. of Geology and Geophysics)
1994-11-01
A general inversion scheme based on a genetic algorithm is developed to invert seismic observations for anisotropic parameters. The technique is applied to the inversion of shear-wave observations from two azimuthal VSP data sets from the Conoco test site in Oklahoma. Horizontal polarizations and time-delays are inverted for hexagonal and orthorhombic symmetries. The model solutions are consistent with previous studies using trial and error matching of full waveform synthetics. The shear-wave splitting observations suggest the presence of a shear-wave line singularity and are consistent with a dipping fracture system which is known to exist at the test site. Application of the inversion scheme prior to full waveform modeling demonstrates that a considerable saving in time is possible while retaining the same degree of accuracy.
Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling
NASA Technical Reports Server (NTRS)
Lohn, Jason; Colombano, Silvano
1997-01-01
We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.
PDE Nozzle Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Billings, Dana; Turner, James E. (Technical Monitor)
2000-01-01
Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.
Birefringent filter design by use of a modified genetic algorithm.
Wen, Mengtao; Yao, Jianping
2006-06-10
A modified genetic algorithm is proposed for the optimization of fiber birefringent filters. The orientation angles and the element lengths are determined by the genetic algorithm to minimize the sidelobe levels of the filters. Being different from the normal genetic algorithm, the algorithm proposed reduces the problem space of the birefringent filter design to achieve faster speed and better performance. The design of 4-, 8-, and 14-section birefringent filters with an improved sidelobe suppression ratio is realized. A 4-section birefringent filter designed with the algorithm is experimentally realized. PMID:16761031
On the scalability of parallel genetic algorithms.
Cantú-Paz, E; Goldberg, D E
1999-01-01
This paper examines the scalability of several types of parallel genetic algorithms (GAs). The objective is to determine the optimal number of processors that can be used by each type to minimize the execution time. The first part of the paper considers algorithms with a single population. The investigation focuses on an implementation where the population is distributed to several processors, but the results are applicable to more common master-slave implementations, where the population is entirely stored in a master processor and multiple slaves are used to evaluate the fitness. The second part of the paper deals with parallel GAs with multiple populations. It first considers a bounding case where the connectivity, the migration rate, and the frequency of migrations are set to their maximal values. Then, arbitrary regular topologies with lower migration rates are considered and the frequency of migrations is set to its lowest value. The investigationis mainly theoretical, but experimental evidence with an additively-decomposable function is included to illustrate the accuracy of the theory. In all cases, the calculations show that the optimal number of processors that minimizes the execution time is directly proportional to the square root of the population size and the fitness evaluation time. Since these two factors usually increase as the domain becomes more difficult, the results of the paper suggest that parallel GAs can integrate large numbers of processors and significantly reduce the execution time of many practical applications. PMID:10578030
Spacecraft Attitude Maneuver Planning Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Kornfeld, Richard P.
2004-01-01
A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.
A Parallel Genetic Algorithm for Automated Electronic Circuit Design
NASA Technical Reports Server (NTRS)
Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris
2000-01-01
issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.
Self-adaptive genetic algorithms with simulated binary crossover.
Deb, K; Beyer, H G
2001-01-01
Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs. PMID:11382356
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.
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…
Specific optimization of genetic algorithm on special algebras
NASA Astrophysics Data System (ADS)
Habiballa, Hashim; Novak, Vilem; Dyba, Martin; Schenk, Jiri
2016-06-01
Searching for complex finite algebras can be succesfully done by the means of genetic algorithm as we showed in former works. This genetic algorithm needs specific optimization of crossover and mutation. We present details about these optimizations which are already implemented in software application for this task - EQCreator.
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…
MM Algorithms for Geometric and Signomial Programming.
Lange, Kenneth; Zhou, Hua
2014-02-01
This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates. PMID:24634545
MM Algorithms for Geometric and Signomial Programming
Lange, Kenneth; Zhou, Hua
2013-01-01
This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates. PMID:24634545
Advanced optimization of permanent magnet wigglers using a genetic algorithm
Hajima, Ryoichi
1995-12-31
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.
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.
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…
Genetic algorithm-based form error evaluation
NASA Astrophysics Data System (ADS)
Cui, Changcai; Li, Bing; Huang, Fugui; Zhang, Rencheng
2007-07-01
Form error evaluation of geometrical products is a nonlinear optimization problem, for which a solution has been attempted by different methods with some complexity. A genetic algorithm (GA) was developed to deal with the problem, which was proved simple to understand and realize, and its key techniques have been investigated in detail. Firstly, the fitness function of GA was discussed emphatically as a bridge between GA and the concrete problems to be solved. Secondly, the real numbers-based representation of the desired solutions in the continual space optimization problem was discussed. Thirdly, many improved evolutionary strategies of GA were described on emphasis. These evolutionary strategies were the selection operation of 'odd number selection plus roulette wheel selection', the crossover operation of 'arithmetic crossover between near relatives and far relatives' and the mutation operation of 'adaptive Gaussian' mutation. After evolutions from generation to generation with the evolutionary strategies, the initial population produced stochastically around the least-squared solutions of the problem would be updated and improved iteratively till the best chromosome or individual of GA appeared. Finally, some examples were given to verify the evolutionary method. Experimental results show that the GA-based method can find desired solutions that are superior to the least-squared solutions except for a few examples in which the GA-based method can obtain similar results to those by the least-squared method. Compared with other optimization techniques, the GA-based method can obtain almost equal results but with less complicated models and computation time.
Random Volumetric MRI Trajectories via Genetic Algorithms
Curtis, Andrew Thomas; Anand, Christopher Kumar
2008-01-01
A pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory is designed for use with balanced steady-state magnetic resonance imaging. Individual arcs are designed independently and do not fit together in the way that multishot spiral, radial or echo-planar trajectories do. Previously, it was shown that second-order cone optimization problems can be defined for each arc independent of the others, that nulling of zeroth and higher moments can be encoded as constraints, and that individual arcs can be optimized in seconds. For use in steady-state imaging, sampling duty cycles are predicted to exceed 95 percent. Using such pseudorandom trajectories, aliasing caused by under-sampling manifests itself as incoherent noise. In this paper, a genetic algorithm (GA) is formulated and numerically evaluated. A large set of arcs is designed using previous methods, and the GA choses particular fit subsets of a given size, corresponding to a desired acquisition time. Numerical simulations of 1 second acquisitions show good detail and acceptable noise for large-volume imaging with 32 coils. PMID:18604305
Closed Loop System Identification with Genetic Algorithms
NASA Technical Reports Server (NTRS)
Whorton, Mark S.
2004-01-01
High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.
Lunar Habitat Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
Genetic algorithm for bundle adjustment in aerial panoramic stitching
NASA Astrophysics Data System (ADS)
Zhang, Chunxiao; Wen, Gaojin; Wu, Chunnan; Wang, Hongmin; Shang, Zhiming; Zhang, Qian
2015-03-01
This paper presents a genetic algorithm for bundle adjustment in aerial panoramic stitching. Compared with the conventional LM (Levenberg-Marquardt) algorithm for bundle adjustment, the proposed bundle adjustment combining the genetic algorithm optimization eliminates the possibility of sticking into the local minimum, and not requires the initial estimation of desired parameters, naturally avoiding the associated steps, that includes the normalization of matches, the computation of homography transformation, the calculations of rotation transformation and the focal length. Since the proposed bundle adjustment is composed of the directional vectors of matches, taking the advantages of genetic algorithm (GA), the Jacobian matrix and the normalization of residual error are not involved in the searching process. The experiment verifies that the proposed bundle adjustment based on the genetic algorithm can yield the global solution even in the unstable aerial imaging condition.
Variable Size Genetic Network Programming
NASA Astrophysics Data System (ADS)
Katagiri, Hironobu; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi
Genetic Network Programming (GNP) is a kind of volutionary methods, which evolves arbitrary directed graph programs. Previously, the program size of GNP was fixed. In the paper, a new method is proposed, where the program size is adaptively changed depending on the frequency of the use of nodes. To control and to decide a program size are important and difficult problems in Evolutionary Computation, especially, a well-known crossover operator tends to cause bloat. We introduce two additional operators, add operator and delete operator, that can change the number of each kind of nodes based on whether a node function is important in the environment or not. Simulation results shows that the proposed method brings about extremely better results compared with ordinary fixed size GNP.
Improving Search Properties in Genetic Programming
NASA Technical Reports Server (NTRS)
Janikow, Cezary Z.; DeWeese, Scott
1997-01-01
With the advancing computer processing capabilities, practical computer applications are mostly limited by the amount of human programming required to accomplish a specific task. This necessary human participation creates many problems, such as dramatically increased cost. To alleviate the problem, computers must become more autonomous. In other words, computers must be capable to program/reprogram themselves to adapt to changing environments/tasks/demands/domains. Evolutionary computation offers potential means, but it must be advanced beyond its current practical limitations. Evolutionary algorithms model nature. They maintain a population of structures representing potential solutions to the problem at hand. These structures undergo a simulated evolution by means of mutation, crossover, and a Darwinian selective pressure. Genetic programming (GP) is the most promising example of an evolutionary algorithm. In GP, the structures that evolve are trees, which is a dramatic departure from previously used representations such as strings in genetic algorithms. The space of potential trees is defined by means of their elements: functions, which label internal nodes, and terminals, which label leaves. By attaching semantic interpretation to those elements, trees can be interpreted as computer programs (given an interpreter), evolved architectures, etc. JSC has begun exploring GP as a potential tool for its long-term project on evolving dextrous robotic capabilities. Last year we identified representation redundancies as the primary source of inefficiency in GP. Subsequently, we proposed a method to use problem constraints to reduce those redundancies, effectively reducing GP complexity. This method was implemented afterwards at the University of Missouri. This summer, we have evaluated the payoff from using problem constraints to reduce search complexity on two classes of problems: learning boolean functions and solving the forward kinematics problem. We have also
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.
Genetic Network Programming with Intron-Like Nodes
NASA Astrophysics Data System (ADS)
Mabu, Shingo; Chen, Yan; Eto, Shinji; Shimada, Kaoru; Hirasawa, Kotaro
Recently, Genetic Network Programming (GNP) has been proposed, which is an extension of Genetic Algorithm(GA) and Genetic Programming(GP). GNP can make compact programs and can memorize the past history in it implicitly, because it expresses the solution by directed graphs and therefore, it can reuse the nodes. In this research, intron-like nodes are introduced for improving the performance of GNP. The aim of introducing intron-like nodes is to use every node as much as possible. It is found from simulations that the intron-like nodes are useful for improving the training speed and generalization ability.
Genetic Algorithm Based Neural Networks for Nonlinear Optimization
Energy Science and Technology Software Center (ESTSC)
1994-09-28
This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less
Aerodynamic optimum design of transonic turbine cascades using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Li, Jun; Feng, Zhenping; Chang, Jianzhong; Shen, Zuda
1997-06-01
This paper presents an aerodynamic optimum design method for transonic turbine cascades based on the Genetic Algorithms coupled to the inviscid flow Euler solver and the boundary-layer calculation. The Genetic Algorithms control the evolution of a population of cascades towards an optimum design. The fitness value of each string is evaluated using the flow solver. The design procedure has been developed and the behavior of the genetic algorithms has been tested. The objective functions of the design examples are the minimum mean-square deviation between the aimed pressure and computed pressure and the minimum amount of user expertise.
A genetic algorithm approach to recognition and data mining
Punch, W.F.; Goodman, E.D.; Min, Pei
1996-12-31
We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.
Investigation of image feature extraction by a genetic algorithm
NASA Astrophysics Data System (ADS)
Brumby, Steven P.; Theiler, James P.; Perkins, Simon J.; Harvey, Neal R.; Szymanski, John J.; Bloch, Jeffrey J.; Mitchell, Melanie
1999-11-01
We describe the implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the
An algorithm for genetic testing of frontotemporal lobar degeneration
Rademakers, R.; Huey, E.D.; Boxer, A.L.; Mayeux, R.; Miller, B.L.; Boeve, B.F.
2011-01-01
Objective: To derive an algorithm for genetic testing of patients with frontotemporal lobar degeneration (FTLD). Methods: A literature search was performed to review the clinical and pathologic phenotypes and family history associated with each FTLD gene. Results: Based on the literature review, an algorithm was developed to allow clinicians to use the clinical and neuroimaging phenotypes of the patient and the family history and autopsy information to decide whether or not genetic testing is warranted, and if so, the order for appropriate tests. Conclusions: Recent findings in genetics, pathology, and imaging allow clinicians to use the clinical presentation of the patient with FTLD to inform genetic testing decisions. PMID:21282594
Successful technical trading agents using genetic programming.
Othling, Andrew S.; Kelly, John A.; Pryor, Richard J.; Farnsworth, Grant V.
2004-10-01
Genetic programming (GP) has proved to be a highly versatile and useful tool for identifying relationships in data for which a more precise theoretical construct is unavailable. In this project, we use a GP search to develop trading strategies for agent based economic models. These strategies use stock prices and technical indicators, such as the moving average convergence/divergence and various exponentially weighted moving averages, to generate buy and sell signals. We analyze the effect of complexity constraints on the strategies as well as the relative performance of various indicators. We also present innovations in the classical genetic programming algorithm that appear to improve convergence for this problem. Technical strategies developed by our GP algorithm can be used to control the behavior of agents in economic simulation packages, such as ASPEN-D, adding variety to the current market fundamentals approach. The exploitation of arbitrage opportunities by technical analysts may help increase the efficiency of the simulated stock market, as it does in the real world. By improving the behavior of simulated stock markets, we can better estimate the effects of shocks to the economy due to terrorism or natural disasters.
Improved genetic algorithm for fast path planning of USV
NASA Astrophysics Data System (ADS)
Cao, Lu
2015-12-01
Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for USV(Unmanned Surface Vehicle), an approach of fast path planning based on voronoi diagram and improved Genetic Algorithm is proposed, which makes use of the principle of hierarchical path planning. First the voronoi diagram is utilized to generate the initial paths and then the optimal path is searched by using the improved Genetic Algorithm, which use multiprocessors parallel computing techniques to improve the traditional genetic algorithm. Simulation results verify that the optimal time is greatly reduced and path planning based on voronoi diagram and the improved Genetic Algorithm is more favorable in the real-time operation.
Optimization of computer-generated binary holograms using genetic algorithms
NASA Astrophysics Data System (ADS)
Cojoc, Dan; Alexandrescu, Adrian
1999-11-01
The aim of this paper is to compare genetic algorithms against direct point oriented coding in the design of binary phase Fourier holograms, computer generated. These are used as fan-out elements for free space optical interconnection. Genetic algorithms are optimization methods which model the natural process of genetic evolution. The configuration of the hologram is encoded to form a chromosome. To start the optimization, a population of different chromosomes randomly generated is considered. The chromosomes compete, mate and mutate until the best chromosome is obtained according to a cost function. After explaining the operators that are used by genetic algorithms, this paper presents two examples with 32 X 32 genes in a chromosome. The crossover type and the number of mutations are shown to be important factors which influence the convergence of the algorithm. GA is demonstrated to be a useful tool to design namely binary phase holograms of complicate structures.
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 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.
Koza, J R
1994-01-01
The recently-developed genetic programming paradigm is used to evolve a computer program to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein. Genetic programming starts with a primordial ooze of randomly generated computer programs composed of available programmatic ingredients and then genetically breeds the population of programs using the Darwinian principle of survival of the fittest and an analog of the naturally occurring genetic operation of crossover (sexual recombination). Automatic function definition enables genetic programming to dynamically create subroutines dynamically during the run. Genetic programming is given a training set of differently-sized protein segments and their correct classification (but no biochemical knowledge, such as hydrophobicity values). Correlation is used as the fitness measure to drive the evolutionary process. The best genetically-evolved program achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6%. This error rate is better than that reported for four other algorithms reported at the First International Conference on Intelligent Systems for Molecular Biology. Our genetically evolved program is an instance of an algorithm discovered by an automated learning paradigm that is superior to that written by human investigators. PMID:7584397
Internal quantum efficiency analysis of solar cell by genetic algorithm
Xiong, Kanglin; Yang, Hui; Lu, Shulong; Zhou, Taofei; Wang, Rongxin; Qiu, Kai; Dong, Jianrong; Jiang, Desheng
2010-11-15
To investigate factors limiting the performance of a GaAs solar cell, genetic algorithm is employed to fit the experimentally measured internal quantum efficiency (IQE) in the full spectra range. The device parameters such as diffusion lengths and surface recombination velocities are extracted. Electron beam induced current (EBIC) is performed in the base region of the cell with obtained diffusion length agreeing with the fit result. The advantage of genetic algorithm is illustrated. (author)
Superscattering of light optimized by a genetic algorithm
Mirzaei, Ali Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.
2014-07-07
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.
Genetic optimization of the HSTAMIDS landmine detection algorithm
NASA Astrophysics Data System (ADS)
Konduri, Ravi K.; Solomon, Geoff Z.; DeJong, Keith; Duvoisin, Herbert A.; Bartosz, Elizabeth E.
2004-09-01
CyTerra's dual sensor HSTAMIDS system has demonstrated exceptional landmine detection capabilities in extensive government-run field tests. Further optimization of the highly successful PentAD-class algorithms for Humanitarian Demining (HD) use (to enhance detection (Pd) and to lower the false alarm rate (FAR)) may be possible. PentAD contains several input parameters, making such optimization computationally intensive. Genetic algorithm techniques, which formerly provided substantial improvement in the detection performance of the metal detector sensor algorithm alone, have been applied to optimize the numerical values of the dual-sensor algorithm parameters. Genetic algorithm techniques have also been applied to choose among several sub-models and fusion techniques to potentially train the HSTAMIDS HD system in new ways. In this presentation we discuss the performance of the resulting algorithm as applied to field data.
Application of genetic algorithms to autopiloting in aerial combat simulation
NASA Astrophysics Data System (ADS)
Kim, Dai Hyun; Erwin, Daniel A.; Kostrzewski, Andrew A.; Kim, Jeongdal; Savant, Gajendra D.
1998-10-01
An autopilot algorithm that controls a fighter aircraft in simulated aerial combat is presented. A fitness function, whose arguments are the control settings of the simulated fighter, is continuously maximized by a fuzzied genetic algorithm. Results are presented for one-to-one combat simulated on a personal computer. Generalization to many-to-many combat is discussed.
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…
Optimization of genomic selection training populations with a genetic algorithm
Technology Transfer Automated Retrieval System (TEKTRAN)
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 ...
Research on Laser Marking Speed Optimization by Using Genetic Algorithm
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
Genetic algorithm and the application for job shop group scheduling
NASA Astrophysics Data System (ADS)
Mao, Jianzhong; Wu, Zhiming
1995-08-01
Genetic algorithm (GA) is a heuristic and random search technique mimicking nature. This paper first presents the basic principle of GA, the definition and the function of the genetic operators, and the principal character of GA. On the basis of these, the paper proposes using GA as a new solution method of the job-shop group scheduling problem, discusses the coded representation method of the feasible solution, and the particular limitation to the genetic operators.
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.
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.
The Genetic Programming of Industrial Microorganisms.
ERIC Educational Resources Information Center
Hopwood, David A.
1981-01-01
Traces the development of the field of industrial microbial genetics, describing a range of techniques for genetic programing. Includes a discussion of site-directed mutagenesis, protoplast fusion, and recombinant DNA manipulations. (CS)
A genetic-based algorithm for personalized resistance training
Kiely, J; Suraci, B; Collins, DJ; de Lorenzo, D; Pickering, C; Grimaldi, KA
2016-01-01
Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective
A genetic-based algorithm for personalized resistance training.
Jones, N; Kiely, J; Suraci, B; Collins, D J; de Lorenzo, D; Pickering, C; Grimaldi, K A
2016-06-01
Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective
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
A parallel genetic algorithm for the set partitioning problem
Levine, D.
1994-05-01
In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. The authors found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulation found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation they found was the difficulty solving problems with many constraints.
Mutual information image registration based on improved bee evolutionary genetic algorithm
NASA Astrophysics Data System (ADS)
Xu, Gang; Tu, Jingzhi
2009-07-01
In recent years, the mutual information is regarded as a more efficient similarity metrics in the image registration. According to the features of mutual information image registration, the Bee Evolution Genetic Algorithm (BEGA) is chosen for optimizing parameters, which imitates swarm mating. Besides, we try our best adaptively set the initial parameters to improve the BEGA. The programming result shows the wonderful precision of the algorithm.
Virus-Evolutionary Liner Genetic Programming
NASA Astrophysics Data System (ADS)
Tamura, Kenji; Mutoh, Atsuko; Nakamura, Tsuyoshi; Itoh, Hidenori
Many kinds of evolutionary methods have been proposed. GA and GP in particular have been demonstrated its effectiveness in various problems these days, and many systems have been proposed. One is Virus-Evolutionary Genetic Algorithm (VE-GA), and the other is Linear Genetic Programming in C (LGPC). Each of systems is reported its performance. VE-GA is the coevolution system that host individual and virus individuals. That can spread schema effectively among the host individuals by using the virus infection and virus incorporation. LGPC implements the GP by representing the individuals to one dimension as if GA. LGPC can reduce a search cost of pointer and save the machine memory, and can reduce the time to implements GP programs. We proposed that a system introduce virus individuals in LGPC, and the analyzed performance of the system at two problems. Our system can spread schema among the population, and search solution effectively. The results of computer simulation show that this system can search for solution depending on LGPC applying problem's character compare with LGPC. A search cost of pointer
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
Atmospheric Downscaling using Genetic Programming
NASA Astrophysics Data System (ADS)
Zerenner, Tanja; Venema, Victor; Simmer, Clemens
2013-04-01
Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and downscaling procedures. Subject of our work is the downscaling scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current downscaling scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for downscaling surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require downscaling, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for downscaling surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and
On Using Surrogates with Genetic Programming.
Hildebrandt, Torsten; Branke, Jürgen
2015-01-01
One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP. PMID:24967694
Forecasting the solar cycle with genetic algorithms
NASA Astrophysics Data System (ADS)
Orfila, A.; Ballester, J. L.; Oliver, R.; Alvarez, A.; Tintoré, J.
2002-04-01
In the past, it has been postulated that the irregular dynamics of the solar cycle may embed a low order chaotic process (Weiss 1988, 1994; Spiegel 1994) which, if true, implies that the future behaviour of solar activity should be predictable. Here, starting from the historical record of Zürich sunspot numbers, we build a dynamical model of the solar cycle which allows us to make a long-term forecast of its behaviour. Firstly, the deterministic part of the time series has been reconstructed using the Singular Spectrum Analysis and then an evolutionary algorithm (Alvarez et al. 2001), based on Darwinian theories of natural selection and survival and ideally suited for non-linear time series, has been applied. Then, the predictive capability of the algorithm has been tested by comparing the behaviour of solar cycles 19-22 with forecasts made with the algorithm, obtaining results which show reasonable agreement with the known behaviour of those cycles. Next, the forecast of the future behaviour of solar cycle 23 has been performed and the results point out that the level of activity during this cycle will be somewhat smaller than in the two previous ones.
Community detection based on modularity and an improved genetic algorithm
NASA Astrophysics Data System (ADS)
Shang, Ronghua; Bai, Jing; Jiao, Licheng; Jin, Chao
2013-03-01
Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.
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.
A genetic algorithm approach in interface and surface structure optimization
Zhang, Jian
2010-01-01
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 material 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.
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.
NASA Astrophysics Data System (ADS)
Vasant, P.; Ganesan, T.; Elamvazuthi, I.
2012-11-01
A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.
Programming Cells: Towardsan automated “Genetic Compiler”
Clancy, Kevin; Voigt, Christopher A.
2010-01-01
I. Summary The increasing scale and sophistication of genetic engineering will necessitate a new generation of computer-aided design (CAD). For large genetic programs, keeping track of the DNA on the level of nucleotides becomes tedious and error prone. To push the size of projects, it is important to abstract the designer from the process of part selection and optimization. The vision is to specify genetic programs in a higher-level language, which a genetic compiler could automatically convert into a DNA sequence. Steps towards this goal include: defining the semantics of the higher-level language, algorithms to select and assemble parts, and biophysical methods to link DNA sequence to function. These will be coupled to graphic design interfaces and simulation packages to aid in the prediction of program dynamics, optimize genes, and scan projects for errors. PMID:20702081
A systematic study of genetic algorithms with genotype editing
Huang, C. F.; Rocha, L. M.
2004-01-01
This paper presents our systematic study on an RNA-editing computational model of Genetic Algorithms (GA). This model is constructed based on several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed by [15]. The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally regulated. The systematic study of this RNA-editing model has shed some light into the evolutionary implications of RNA editing and how to select proper RNA editors for design of more robust GAS. The results will also show promising applications to complex real-world problems. We expect that the framework proposed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation.
Acoustic design of rotor blades using a genetic algorithm
NASA Technical Reports Server (NTRS)
Wells, V. L.; Han, A. Y.; Crossley, W. A.
1995-01-01
A genetic algorithm coupled with a simplified acoustic analysis was used to generate low-noise rotor blade designs. The model includes thickness, steady loading and blade-vortex interaction noise estimates. The paper presents solutions for several variations in the fitness function, including thickness noise only, loading noise only, and combinations of the noise types. Preliminary results indicate that the analysis provides reasonable assessments of the noise produced, and that genetic algorithm successfully searches for 'good' designs. The results show that, for a given required thrust coefficient, proper blade design can noticeably reduce the noise produced at some expense to the power requirements.
Use of a genetic algorithm to analyze robust stability problems
Murdock, T.M.; Schmitendorf, W.E.; Forrest, S.
1990-01-01
This note resents a genetic algorithm technique for testing the stability of a characteristic polynomial whose coefficients are functions of unknown but bounded parameters. This technique is fast and can handle a large number of parametric uncertainties. We also use this method to determine robust stability margins for uncertain polynomials. Several benchmark examples are included to illustrate the two uses of the algorithm. 27 refs., 4 figs.
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.
Genetic algorithm for extracting rules in discrete domain
Neruda, R.
1995-09-20
We propose a genetic algorithm that evolves families of rules from a set of examples. Inputs and outputs of the problem are discrete and nominal values which makes it difficult to use alternative learning methods that implicitly regard a metric space. A way how to encode sets of rules is presented together with special variants of genetic operators suitable for this encoding. The solution found by means of this process can be used as a core of a rule-based expert system.
Polyglot Programming in Applications Used for Genetic Data Analysis
Nowak, Robert M.
2014-01-01
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development. PMID:25197633
Polyglot programming in applications used for genetic data analysis.
Nowak, Robert M
2014-01-01
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development. PMID:25197633
RNA-RNA interaction prediction using genetic algorithm
2014-01-01
Background RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time. Results In this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNA-RNA interaction. The proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in comparison to the other state-of-the-art algorithms. In the proposed algorithm, each individual is a secondary structure of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two interacting RNAs by using crossover and mutation operations. Conclusions This algorithm is properly employed for joint secondary structure prediction. The results achieved on a set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each iteration is as efficient as the other approaches. PMID:25114714
Structural pattern recognition using genetic algorithms with specialized operators.
Khoo, K G; Suganthan, P N
2003-01-01
This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In this study, potential solutions are represented by integer strings indicating the mapping between scene and model vertices. The fitness of each solution string is computed by accumulating the similarity between the unary and binary attributes of the matched vertex pairs. We propose novel crossover and mutation operators, specifically for this problem. With these specialized genetic operators, the proposed algorithm converges to better quality solutions at a faster rate than the standard genetic algorithm (SGA). In addition, the proposed algorithm is also capable of recognizing multiple instances of any model object. An efficient pose-clustering algorithm is used to eliminate occasional wrong mappings and to determine the presence/pose of the model in the scene. We demonstrate the superior performance of our proposed algorithm using extensive experimental results. PMID:18238167
Optimal caching algorithm based on dynamic programming
NASA Astrophysics Data System (ADS)
Guo, Changjie; Xiang, Zhe; Zhong, Yuzhuo; Long, Jidong
2001-07-01
With the dramatic growth of multimedia streams, the efficient distribution of stored videos has become a major concern. There are two basic caching strategies: the whole caching strategy and the caching strategy based on layered encoded video, the latter can satisfy the requirement of the highly heterogeneous access to the Internet. Conventional caching strategies assign each object a cache gain by calculating popularity or density popularity, and determine which videos and which layers should be cached. In this paper, we first investigate the delivery model of stored video based on proxy, and propose two novel caching algorithms, DPLayer (for layered encoded caching scheme) and DPWhole (for whole caching scheme) for multimedia proxy caching. The two algorithms are based on the resource allocation model of dynamic programming to select the optimal subset of objects to be cached in proxy. Simulation proved that our algorithms achieve better performance than other existing schemes. We also analyze the computational complexity and space complexity of the algorithms, and introduce a regulative parameter to compress the states space of the dynamic programming problem and reduce the complexity of algorithms.
Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing
Gong, Dunwei
2014-01-01
The application of genetic algorithms in automatically generating test data has aroused broad concerns and obtained delightful achievements in recent years. However, the efficiency of genetic algorithm-based test data generation for path testing needs to be further improved. In this paper, we establish a mathematical model of generating test data for multiple paths coverage. Then, a multipopulation genetic algorithm with individual sharing is presented to solve the established model. We not only analyzed the performance of the proposed method theoretically, but also applied it to various programs under test. The experimental results show that the proposed method can improve the efficiency of generating test data for many paths' coverage significantly. PMID:25691894
Biased Random-Key Genetic Algorithms for the Winner Determination Problem in Combinatorial Auctions.
de Andrade, Carlos Eduardo; Toso, Rodrigo Franco; Resende, Mauricio G C; Miyazawa, Flávio Keidi
2015-01-01
In this paper we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions. PMID:25299242
Genetic algorithms, path relinking, and the flowshop sequencing problem.
Reeves, C R; Yamada, T
1998-01-01
In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly. PMID:10021740
Genetic algorithm for chromaticity correction in diffraction limited storage rings
NASA Astrophysics Data System (ADS)
Ehrlichman, M. P.
2016-04-01
A multiobjective genetic algorithm is developed for optimizing nonlinearities in diffraction limited storage rings. This algorithm determines sextupole and octupole strengths for chromaticity correction that deliver optimized dynamic aperture and beam lifetime. The algorithm makes use of dominance constraints to breed desirable properties into the early generations. The momentum aperture is optimized indirectly by constraining the chromatic tune footprint and optimizing the off-energy dynamic aperture. The result is an effective and computationally efficient technique for correcting chromaticity in a storage ring while maintaining optimal dynamic aperture and beam lifetime.
Genetic-Annealing Algorithm in Grid Environment for Scheduling Problems
NASA Astrophysics Data System (ADS)
Cruz-Chávez, Marco Antonio; Rodríguez-León, Abelardo; Ávila-Melgar, Erika Yesenia; Juárez-Pérez, Fredy; Cruz-Rosales, Martín H.; Rivera-López, Rafael
This paper presents a parallel hybrid evolutionary algorithm executed in a grid environment. The algorithm executes local searches using simulated annealing within a Genetic Algorithm to solve the job shop scheduling problem. Experimental results of the algorithm obtained in the "Tarantula MiniGrid" are shown. Tarantula was implemented by linking two clusters from different geographic locations in Mexico (Morelos-Veracruz). The technique used to link the two clusters and configure the Tarantula MiniGrid is described. The effects of latency in communication between the two clusters are discussed. It is shown that the evolutionary algorithm presented is more efficient working in Grid environments because it can carry out major exploration and exploitation of the solution space.
The study of Genetic Algorithm by Hierarchical Coded for the MMRCPSP
NASA Astrophysics Data System (ADS)
Shi-man, Xie; Chen, Jian-wei; Xuan, Zhao-yan
In order to solve the problem of Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP), this paper suggests Genetic Algorithm (GA) by hierarchical coded. In the first layer, the chromosomes are used to choose the activity sequence. In the second layer, the chromosomes are used to decide the combination of activity modes. The chromosomes produced by the Activities Resource Competition Relation (ARCR) are coded by binary code. That is to say, the subsequent operation will be improved by mature algorithm including selection, crossover and mutation. Finally, programing used PSBLIB standard data shows that this algorithm is feasible.
Filter Circuit Design by Parallel Genetic Programming
NASA Astrophysics Data System (ADS)
Yano, Yuichi; Kato, Toshiji; Inoue, Kaoru; Miki, Mitsunori
Genetic Programming (GP) is an extension of Genetic Algorithm(GA) to handle more structural problems. In this paper, an approach to filter circuit design by GP is proposed. By designing a gene which includes not only the parameters of consisting elements, but also the structural information of the circuit, it becomes possible to apply the proposed approach to various types of filter circuits. GP depends much on trial and error due to its probabilitic nature. To decrease this uncertainty and ensure the progress of the evolution, Parallel GP with multiple populations with the island model is also proposed. An MPI-based cluster system is used for realization of this parallel computing where each island correspondsd to each node. A lowpass and an asymmetric bandpass filters are designed. One hundred times of trials for multiple populations with and without migrations are tested in the design of lowpass filter to confirm the validity of the proposed method. In the asymmetric bandpass filter design, the results are compared with those of the circuit designed by hand to confirm the effectiveness of the proposed method. The proposed approach is applicable to various types of filter circuits. It can contribute to an automated design procedure, where it would require a expirenced designer if done by hand. It is also possible to obtain a new circuit design which would not be possible if done by hand.
The multi-niche crowding genetic algorithm: Analysis and applications
Cedeno, W.
1995-09-01
The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.
Hybrid methods using genetic algorithms for global optimization.
Renders, J M; Flasse, S P
1996-01-01
This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from genetic algorithms and "hill-climbing" methods in order to find a better compromise to the trade-off. Inspired by biology and especially by the manner in which living beings adapt themselves to their environment, these hybrid methods involve two interwoven levels of optimization, namely evolution (genetic algorithms) and individual learning (Quasi-Newton), which cooperate in a global process of optimization. One of these hybrid methods appears to join the group of state-of-the-art global optimization methods: it combines the reliability properties of the genetic algorithms with the accuracy of Quasi-Newton method, while requiring a computation time only slightly higher than the latter. PMID:18263027
Experiences with the PGAPack Parallel Genetic Algorithm library
Levine, D.; Hallstrom, P.; Noelle, D.; Walenz, B.
1997-07-01
PGAPack is the first widely distributed parallel genetic algorithm library. Since its release, several thousand copies have been distributed worldwide to interested users. In this paper we discuss the key components of the PGAPack design philosophy and present a number of application examples that use PGAPack.
USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES
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 ...
Optimization of reliability allocation strategies through use of genetic algorithms
Campbell, J.E.; Painton, L.A.
1996-08-01
This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.
Crossover Improvement for the Genetic Algorithm in Information Retrieval.
ERIC Educational Resources Information Center
Vrajitoru, Dana
1998-01-01
In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…
Applying Genetic Algorithms To Query Optimization in Document Retrieval.
ERIC Educational Resources Information Center
Horng, Jorng-Tzong; Yeh, Ching-Chang
2000-01-01
Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)
A parallel genetic algorithm for the set partitioning problem
Levine, D.
1996-12-31
This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36, 699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
Concurrent genetic algorithms for optimization of large structures
Adeli, H.; Cheng, N. )
1994-07-01
In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high-rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high-performance computers such as the Cray Y-MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high-rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35-story tower (with 1,262 elements and 936 degrees of freedom), using eight processors. 9 refs.
Optimization of phononic filters via genetic algorithms
NASA Astrophysics Data System (ADS)
Hussein, M. I.; El-Beltagy, M. A.
2007-12-01
A phononic crystal is commonly characterized by its dispersive frequency spectrum. With appropriate spatial distribution of the constituent material phases, spectral stop bands could be generated. Moreover, it is possible to control the number, the width, and the location of these bands within a frequency range of interest. This study aims at exploring the relationship between unit cell configuration and frequency spectrum characteristics. Focusing on 1D layered phononic crystals, and longitudinal wave propagation in the direction normal to the layering, the unit cell features of interest are the number of layers and the material phase and relative thickness of each layer. An evolutionary search for binary- and ternary-phase cell designs exhibiting a series of stop bands at predetermined frequencies is conducted. A specially formulated representation and set of genetic operators that break the symmetries in the problem are developed for this purpose. An array of optimal designs for a range of ratios in Young's modulus and density are obtained and the corresponding objective values (the degrees to which the resulting bands match the predetermined targets) are examined as a function of these ratios. It is shown that a rather complex filtering objective could be met with a high degree of success. Structures composed of the designed phononic crystals are excellent candidates for use in a wide range of applications including sound and vibration filtering.
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.
Virus evolutionary genetic algorithm for task collaboration of logistics distribution
NASA Astrophysics Data System (ADS)
Ning, Fanghua; Chen, Zichen; Xiong, Li
2005-12-01
In order to achieve JIT (Just-In-Time) level and clients' maximum satisfaction in logistics collaboration, a Virus Evolutionary Genetic Algorithm (VEGA) was put forward under double constraints of logistics resource and operation sequence. Based on mathematic description of a multiple objective function, the algorithm was designed to schedule logistics tasks with different due dates and allocate them to network members. By introducing a penalty item, make span and customers' satisfaction were expressed in fitness function. And a dynamic adaptive probability of infection was used to improve performance of local search. Compared to standard Genetic Algorithm (GA), experimental result illustrates the performance superiority of VEGA. So the VEGA can provide a powerful decision-making technique for optimizing resource configuration in logistics network.
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
Large-scale sequential quadratic programming algorithms
Eldersveld, S.K.
1992-09-01
The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.
Scheduling trucks in container terminals using a genetic algorithm
NASA Astrophysics Data System (ADS)
Ng, W. C.; Mak, K. L.; Zhang, Y. X.
2007-01-01
Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other six GAs.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500
Applying fuzzy theory and genetic algorithm to interpolate precipitation
NASA Astrophysics Data System (ADS)
Chang, C. L.; Lo, S. L.; Yu, S. L.
2005-11-01
A watershed management program is usually based on the results of watershed modeling. Accurate modeling results are decided by the appropriate parameters and input data. Rainfall is the most important input for watershed modeling. Precipitation characteristics, such as rainfall intensity and duration, usually exhibit significant spatial variation, even within small watersheds. Therefore, properly describing the spatial variation of rainfall is essential for predicting the water movement in a watershed. Varied circumstances require a variety of suitable methods for interpolating and estimating precipitation. In this study, a modified method, combining the inverse distance method and fuzzy theory, was applied to precipitation interpolation. Meanwhile, genetic algorithm (GA) was used to determine the parameters of fuzzy membership functions, which represent the relationship between the location without rainfall records and its surrounding rainfall gauges. The objective in the optimization process is to minimize the estimated error of precipitation. The results show that the estimated error is usually reduced by this method. Particularly, when there are large and irregular elevation differences between the interpolated area and its vicinal rainfall gauging stations, it is important to consider the effect of elevation differences, in addition to the effect of horizontal distances. Reliable modeling results can substantially lower the cost for the watershed management strategy.
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-05-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al{sub n} (n up to 23) were performed using a genetic algorithm coupled with a tight-binding potential. Second, a genetic algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of {radical}3 x {radical}3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
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.
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm.
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770
Evaluation of algorithms used to order markers on genetic maps.
Mollinari, M; Margarido, G R A; Vencovsky, R; Garcia, A A F
2009-12-01
When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with 100 and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results. PMID:19639011
Efficient Improvement of Silage Additives by Using Genetic Algorithms
Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.
2000-01-01
The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
NASA Astrophysics Data System (ADS)
Cantó, J.; Curiel, S.; Martínez-Gómez, E.
2009-07-01
Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.
Automatic reactor model synthesis with genetic programming.
Dürrenmatt, David J; Gujer, Willi
2012-01-01
Successful modeling of wastewater treatment plant (WWTP) processes requires an accurate description of the plant hydraulics. Common methods such as tracer experiments are difficult and costly and thus have limited applicability in practice; engineers are often forced to rely on their experience only. An implementation of grammar-based genetic programming with an encoding to represent hydraulic reactor models as program trees should fill this gap: The encoding enables the algorithm to construct arbitrary reactor models compatible with common software used for WWTP modeling by linking building blocks, such as continuous stirred-tank reactors. Discharge measurements and influent and effluent concentrations are the only required inputs. As shown in a synthetic example, the technique can be used to identify a set of reactor models that perform equally well. Instead of being guided by experience, the most suitable model can now be chosen by the engineer from the set. In a second example, temperature measurements at the influent and effluent of a primary clarifier are used to generate a reactor model. A virtual tracer experiment performed on the reactor model has good agreement with a tracer experiment performed on-site. PMID:22277238
NASA Astrophysics Data System (ADS)
Karthik, Victor U.; Sivasuthan, Sivamayam; Hoole, Samuel Ratnajeevan H.
2014-02-01
The computational algorithms for device synthesis and nondestructive evaluation (NDE) are often the same. In both we have a goal - a particular field configuration yielding the design performance in synthesis or to match exterior measurements in NDE. The geometry of the design or the postulated interior defect is then computed. Several optimization methods are available for this. The most efficient like conjugate gradients are very complex to program for the required derivative information. The least efficient zeroth order algorithms like the genetic algorithm take much computational time but little programming effort. This paper reports launching a Genetic Algorithm kernel on thousands of compute unified device architecture (CUDA) threads exploiting the NVIDIA graphics processing unit (GPU) architecture. The efficiency of parallelization, although below that on shared memory supercomputer architectures, is quite effective in cutting down solution time into the realm of the practicable. We carry this further into multi-physics electro-heat problems where the parameters of description are in the electrical problem and the object function in the thermal problem. Indeed, this is where the derivative of the object function in the heat problem with respect to the parameters in the electrical problem is the most difficult to compute for gradient methods, and where the genetic algorithm is most easily implemented.
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.
Selection Intensity in Genetic Algorithms with Generation Gaps
Cantu-Paz, E.
2000-01-19
This paper presents calculations of the selection intensity of common selection and replacement methods used in genetic algorithms (GAs) with generation gaps. The selection intensity measures the increase of the average fitness of the population after selection, and it can be used to predict the average fitness of the population at each iteration as well as the number of steps until the population converges to a unique solution. In addition, the theory explains the fast convergence of some algorithms with small generation gaps. The accuracy of the calculations was verified experimentally with a simple test function. The results of this study facilitate comparisons between different algorithms, and provide a tool to adjust the selection pressure, which is indispensable to obtain robust algorithms.
The ordered clustered travelling salesman problem: a hybrid genetic algorithm.
Ahmed, Zakir Hussain
2014-01-01
The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148
A 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.
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.
Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
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
Using genetic algorithms to construct a network for financial prediction
NASA Astrophysics Data System (ADS)
Patel, Devesh
1996-03-01
Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. Artificial neural networks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. Neural networks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.
Human-competitive evolution of quantum computing artefacts by Genetic Programming.
Massey, Paul; Clark, John A; Stepney, Susan
2006-01-01
We show how Genetic Programming (GP) can be used to evolve useful quantum computing artefacts of increasing sophistication and usefulness: firstly specific quantum circuits, then quantum programs, and finally system-independent quantum algorithms. We conclude the paper by presenting a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP. PMID:16536889
Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization
NASA Technical Reports Server (NTRS)
Shaykhian, Gholam Ali; Sen, S. K.
2007-01-01
Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *
Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm
NASA Astrophysics Data System (ADS)
Şencan Şahin, Arzu
2012-11-01
Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.
NASA Astrophysics Data System (ADS)
Salami, M. J. E.; Tijani, I. B.; Abdullateef, A. I.; Aibinu, M. A.
2013-12-01
A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development.
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
Genetic algorithms and their use in Geophysical Problems
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 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. 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
A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects
Alimi, Roger; Weiss, Eyal; Ram-Cohen, Tsuriel; Geron, Nir; Yogev, Idan
2015-01-01
A dedicated Genetic Algorithm (GA) has been developed to localize the trajectory of ferromagnetic moving objects within a bounded perimeter. Localization of moving ferromagnetic objects is an important tool because it can be employed in situations when the object is obscured. This work is innovative for two main reasons: first, the GA has been tuned to provide an accurate and fast solution to the inverse magnetic field equations problem. Second, the algorithm has been successfully tested using real-life experimental data. Very accurate trajectory localization estimations were obtained over a wide range of scenarios. PMID:26393598
Detection of parametric curves based on genetic algorithm
NASA Astrophysics Data System (ADS)
Li, Haimin; Wu, Chengke
1998-09-01
Detection of curves with special shapes has been put on great interest in the fields of image processing and recognition. Some commonly used algorithms such as Hough Transform and Generalized Radon Transform are global search methods. When the number of parameters increases, their efficiencies decrease rapidly because of the expansion of parameter space. To solve this problem, a new method based on Genetic Algorithm is presented which combines a local search procedure to improve its performance. Experimental results show that the proposed method improves search efficiency greatly.
Mass spectrometry cancer data classification using wavelets and genetic algorithm.
Nguyen, Thanh; Nahavandi, Saeid; Creighton, Douglas; Khosravi, Abbas
2015-12-21
This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners. PMID:26611346
a Genetic Algorithm for Urban Transit Routing Problem
NASA Astrophysics Data System (ADS)
Chew, Joanne Suk Chun; Lee, Lai Soon
The Urban Transit Routing Problem (UTRP) involves solving a set of transit route networks, which proved to be a highly complex multi-constrained problem. In this study, a bus route network to find an efficient network to meet customer demands given information on link travel times is considered. An evolutionary optimization technique, called Genetic Algorithm is proposed to solve the UTRP. The main objective is to minimize the passenger costs where the quality of the route sets is evaluated by a set of parameters. Initial computational experiments show that the proposed algorithm performs better than the benchmark results for Mandl's problems.
Optimization of multilayer cylindrical cloaks using genetic algorithms and NEWUOA
NASA Astrophysics Data System (ADS)
Sakr, Ahmed A.; Abdelmageed, Alaa K.
2016-06-01
The problem of minimizing the scattering from a multilayer cylindrical cloak is studied. Both TM and TE polarizations are considered. A two-stage optimization procedure using genetic algorithms and NEWUOA (new unconstrained optimization algorithm) is adopted for realizing the cloak using homogeneous isotropic layers. The layers are arranged such that they follow a repeated pattern of alternating DPS and DNG materials. The results show that a good level of invisibility can be realized using a reasonable number of layers. Maintaining the cloak performance over a finite range of frequencies without sacrificing the level of invisibility is achieved.
Genetic Algorithm for Initial Orbit Determination with Too Short Arc
NASA Astrophysics Data System (ADS)
Li, X. R.; Wang, X.
2016-01-01
The sky surveys of space objects have obtained a huge quantity of too-short-arc (TSA) observation data. However, the classical method of initial orbit determination (IOD) can hardly get reasonable results for the TSAs. The IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selection of optimizing variables as well as the corresponding genetic operator for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.
NASA Astrophysics Data System (ADS)
Windarto, Indratno, S. W.; Nuraini, N.; Soewono, E.
2014-02-01
Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The algorithm begins by defining the optimization variables, defining the cost function (in a minimization problem) or the fitness function (in a maximization problem) and selecting genetic algorithm parameters. The main procedures in genetic algorithm are generating initial population, selecting some chromosomes (individual) as parent's individual, mating, and mutation. In this paper, binary and continuous genetic algorithms were implemented to estimate growth rate and carrying capacity parameter from poultry data cited from literature. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, both algorithms can estimate these parameters well. Suitable range for mutation rate in continuous genetic algorithm is wider than the binary one.
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.
Optimal Design of Geodetic Network Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vajedian, Sanaz; Bagheri, Hosein
2010-05-01
A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied
Atmospheric Downscaling using Genetic Programming
NASA Astrophysics Data System (ADS)
Zerenner, T.; Venema, V.; Simmer, C.
2013-12-01
The coupling of models for the different components of the soil-vegetation-atmosphere system is required to understand component interactions and feedback processes. The Transregional Collaborative Research Center 32 (TR 32) has developed a coupled modeling platform, TerrSysMP, consisting of the atmospheric model COSMO, the land-surface model CLM, and the hydrological model ParFlow. These component models are usually operated at different resolutions in space and time owing to the dominant processes. These different scales should also be considered in the coupling mode, because it is for instance unfeasible to run the computationally quite expensive atmospheric models at the usually much higher spatial resolution required by hydrological models. Thus up- and downscaling procedures are required at the interface between atmospheric model and land-surface/subsurface models. Here we present an advanced atmospheric downscaling scheme, that creates realistic fine-scale fields (e.g. 400 m resolution) of the atmospheric state variables from the coarse atmospheric model output (e.g. 2.8 km resolution). The mixed physical/statistical scheme is developed from a training data set of high-resolution atmospheric model runs covering a range different weather conditions using Genetic Programming (GP). GP originates from machine learning: From a set of functions (arithmetic expressions, IF-statements, etc.) and terminals (constants or variables) GP generates potential solutions to a given problem while minimizing a fitness or cost function. We use a multi-objective approach that aims at fitting spatial structures, spatially distributed variance and spatio-temporal correlation of the fields. We account for the spatio-temporal nature of the data in two ways. On the one hand we offer GP potential predictors, which are based on our physical understanding of the atmospheric processes involved (spatial and temporal gradients, etc.). On the other hand we include functions operating on
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.
Genetic algorithm application in optimization of wireless sensor networks.
Norouzi, Ali; Zaim, A Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
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.
Designing a competent simple genetic algorithm for search and optimization
NASA Astrophysics Data System (ADS)
Reed, Patrick; Minsker, Barbara; Goldberg, David E.
2000-12-01
Simple genetic algorithms have been used to solve many water resources problems, but specifying the parameters that control how adaptive search is performed can be a difficult and time-consuming trial-and-error process. However, theoretical relationships for population sizing and timescale analysis have been developed that can provide pragmatic tools for vastly limiting the number of parameter combinations that must be considered. The purpose of this technical note is to summarize these relationships for the water resources community and to illustrate their practical utility in a long-term groundwater monitoring design application. These relationships, which model the effects of the primary operators of a simple genetic algorithm (selection, recombination, and mutation), provide a highly efficient method for ensuring convergence to near-optimal or optimal solutions. Application of the method to a monitoring design test case identified robust parameter values using only three trial runs.
Genetic Algorithm Application in Optimization of Wireless Sensor Networks
Norouzi, Ali; Zaim, A. Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235
Design of PID-type controllers using multiobjective genetic algorithms.
Herreros, Alberto; Baeyens, Enrique; Perán, José R
2002-10-01
The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases. PMID:12398277
Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control
NASA Technical Reports Server (NTRS)
Rogers, James L.
2004-01-01
The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.
GAz: a genetic algorithm for photometric redshift estimation
NASA Astrophysics Data System (ADS)
Hogan, Robert; Fairbairn, Malcolm; Seeburn, Navin
2015-05-01
We present a new approach to the problem of estimating the redshift of galaxies from photometric data. The approach uses a genetic algorithm combined with non-linear regression to model the 2SLAQ LRG data set with SDSS DR7 photometry. The genetic algorithm explores the very large space of high order polynomials while only requiring optimization of a small number of terms. We find a σrms = 0.0408 ± 0.0006 for redshifts in the range 0.4 < z < 0.7. These results are competitive with the current state-of-the-art but can be presented simply as a polynomial which does not require the user to run any code. We demonstrate that the method generalizes well to other data sets and redshift ranges by testing it on SDSS DR11 and on simulated data. For other data sets or applications the code has been made available at https://github.com/rbrthogan/GAz.