Markov chain analysis of genetic algorithms applied to fitness functions perturbed concurrently by additive and multiplicative noise

We analyze the transition and convergence properties of genetic algorithms (GAs) applied to fitness functions perturbed concurrently by additive and multiplicative noise. Both additive noise and multiplicative noise are assumed to take on finitely many values. We explicitly construct a Markov chain...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 51; no. 2; pp. 601 - 622
Main Author:	Nakama, Takehiko
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.03.2012 Springer Nature B.V
Subjects:	Additives Algorithms Chains Chromosomes Convex and Discrete Geometry Elitism Expected values Genetic algorithms Investigations Management Science Markov analysis Markov chains Mathematical models Mathematics Mathematics and Statistics Mutation Noise Operations Research Operations Research/Decision Theory Optimization Population Probability Statistics Studies Markov chain analysis Evolutionary computation Perturbed fitness functions Additive and multiplicative noise Noisy environments Genetic algorithms Convergence analysis
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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