Pure Strategy or Mixed Strategy?

Jun He, Feidun He, Hongbin Dong

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

18 Citations (SciVal)

Abstract

Mixed strategy evolutionary algorithms (EAs) aim at integrating several mutation operators into a single algorithm. However no analysis has been made to answer the theoretical question: whether and when is the performance of mixed strategy EAs better than that of pure strategy EAs? In this paper, asymptotic convergence rate and asymptotic hitting time are proposed to measure the performance of EAs. It is proven that the asymptotic convergence rate and asymptotic hitting time of any mixed strategy (1+1) EA consisting of several mutation operators is not worse than that of the worst pure strategy (1+1) EA using only one mutation operator. Furthermore it is proven that if these mutation operators are mutually complementary, then it is possible to design a mixed strategy (1+1) EA whose performance is better than that of any pure strategy (1+1) EA using only one mutation operator.
Original languageEnglish
Title of host publicationEvolutionary Computation in Combinatorial Optimization
Subtitle of host publication12th European Conference, EvoCOP 2012, Málaga, Spain, April 11-13, 2012. Proceedings
EditorsJin-Kao Hao, Martin Middendorf
PublisherSpringer Nature
Pages218-229
Volume7245
ISBN (Electronic)978-3-642-29124-1
ISBN (Print)978-3-642-29123-4, 3642291236
DOIs
Publication statusPublished - 28 Mar 2012

Publication series

NameEvolutionary Computation in Combinatorial Optimization
Volume7245
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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