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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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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The original schema theorem (an inequality) has been replaced by an equality that determines the expected next generation for a simple genetic algorithm.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The original schema theorem (an inequality) has been replaced by an equality that determines the expected next generation for a simple genetic algorithm.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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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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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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Neural Computing and Applications, 2009
Random individual initialization tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to fix, which consume too much computation and lose many good genes.
Qijian Chen +2 more
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Random individual initialization tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to fix, which consume too much computation and lose many good genes.
Qijian Chen +2 more
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Hierarchical Genetic Algorithms
2004Current Genetic Algorithms can efficiently address order-k separable problems, in which the order of the linkage is restricted to a low value k. Outside this class, there exist hierarchical problems that cannot be addressed by current genetic algorithms, yet can be addressed efficiently in principle by exploiting hierarchy.
de Jong, E.D. +2 more
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