Abstract
Identifying the optimal settings for crossover probability p c and mutation probability p m is of an important problem to improve the convergence performance of GAs. In this paper, we modelled genetic algorithms as controlled Markov chain processes, whose transition depend on control parameters (probabilities of crossover and mutation). A stochastic optimization problem is formed based on the performance index of populations during the genetic search, in order to find the optimal values of control parameters so that the performance index is maximized. We have shown theoretically the existence of the optimal control parameters in genetic search and proved that, for the stochastic optimization problem, there exists a pure deterministic strategy which is at least as good as any other pure or mixed (randomized) strategy.
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© 1999 Springer-Verlag Berlin Heidelberg
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Cao, Y., Cao, L. (1999). Controlled Markov Chain Optimization of Genetic Algorithms. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_22
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DOI: https://doi.org/10.1007/3-540-48774-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66050-7
Online ISBN: 978-3-540-48774-6
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