Results 211 to 220 of about 263,270 (261)
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A Genetic Engineering Approach to Genetic Algorithms
Evolutionary Computation, 2001We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful ...
John S. Gero, Vladimir A. Kazakov
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Genetic algorithms for genetic mapping
1998Constructing genetic maps is a prerequisite for most in-depth genetic studies of an organism. The problem of constructing reliable genetic maps for any organism can be considered as a complex optimization problem with both discrete and continuous parameters. This paper shows how genetic algorithms can been used to tackle this problem on simple pedigree.
Gaspin, Christine, Schiex, Thomas
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Genetic algorithms in chemistry
Journal of Chromatography A, 2007Genetic algorithms (GAs) are a quite recent technique of optimization, whose basic concept is mimicking the evolution of a species, according to the Darwinian theory of the "survival of the fittest." The application of genetic algorithms to complex problems usually produces much better results than those obtained by the standard techniques.
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Journal of Heuristics, 2007
Based on some phenomena from human society and nature, we propose a binary affinity genetic algorithm (aGA) by adopting the following strategies: the population is adaptively updated to avoid stagnation; the newly generated individuals will be ensured to survive for some generations in order for them to have time to show their good genes; new ...
Xinchao Zhao, Xiao-Shan Gao
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Based on some phenomena from human society and nature, we propose a binary affinity genetic algorithm (aGA) by adopting the following strategies: the population is adaptively updated to avoid stagnation; the newly generated individuals will be ensured to survive for some generations in order for them to have time to show their good genes; new ...
Xinchao Zhao, Xiao-Shan Gao
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An alternative Genetic Algorithm
2006This paper presents a new Genetic Algorithm (GA), called Alternative Genetic Algorithm (AGA) which has been defined to facilitate theoretical investigations. We have shown that both AGA and the usual GA (UGA) obey similar difference equations. However, theoretical investigations on the AGA are much simpler than on the UGA. For the AGA, we can derive as
Hesser, Jürgen, Männer, Reinhard
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2009 IEEE Congress on Evolutionary Computation, 2009
Genetic algorithms (GAs) are classical evolutionary computation methods, which have a wild application prospect. This paper proposes an improved genetic algorithm, named the isoline genetic algorithm (IGA), for numerical optimization. The proposed algorithm utilizes the population to model isolines of fitness in the search space.
Ying Lin 0001, Jun Zhang 0003
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Genetic algorithms (GAs) are classical evolutionary computation methods, which have a wild application prospect. This paper proposes an improved genetic algorithm, named the isoline genetic algorithm (IGA), for numerical optimization. The proposed algorithm utilizes the population to model isolines of fitness in the search space.
Ying Lin 0001, Jun Zhang 0003
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Evolutionary Computation, 1994
The original schema theorem (an inequality) has been replaced by an equality that determines the expected next generation for a simple genetic algorithm. This has made possible the computation of the trajectory of expected next generations. Visualization of these evolutionary trajectories beginning from different initial populations has led to the ...
Jenny Juliany, Michael D. Vose
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The original schema theorem (an inequality) has been replaced by an equality that determines the expected next generation for a simple genetic algorithm. This has made possible the computation of the trajectory of expected next generations. Visualization of these evolutionary trajectories beginning from different initial populations has led to the ...
Jenny Juliany, Michael D. Vose
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Optimization of Genetic Algorithms by Genetic Algorithms
1993This paper presents an approach to determine the optimal Genetic Algorithm (GA), i.e. the most preferable type of genetic operators and their parameter settings, for a given problem. The basic idea is to consider the search for the best GA as an optimization problem and use another GA to solve it. As a consequence, a primary GA operates on a population
Bernd Freisleben, Michael Härtfelder
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Genetic Algorithms-a Tool for OR?
Journal of the Operational Research Society, 1996Summary: Compared with other metaheuristic techniques such as simulated annealing and tabu search, research into the use of genetic algorithms for the solution of OR problems is still in its infancy. This paper provides an introduction to genetic algorithms and their use in the solution of both classical and practical operational research problems ...
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Computer, 1994
Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice.
Srinivas, M, Patnaik, LM
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Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice.
Srinivas, M, Patnaik, LM
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