Co-Evolutionary Algorithms Based on Mixed Strategy

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Author(s): Wei Hou (Harbin Engineering University & Northeast Agricultural University, China), HongBin Dong (Harbin Engineering University, China)and GuiSheng Yin (Harbin Engineering University, China)
Copyright: 2013
Pages: 14
Source title: Interdisciplinary Advances in Information Technology Research
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-4666-3625-5.ch006

Keywords: Information Science Reference / IT Research & Theory / IT Research and Theory / Library & Information Science

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Abstract

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strategy set consists of cuboid, two-points and heuristic crossover operators. The novel algorithms automatically select crossover and/or mutation operators from a given mixed strategy set, and improve the evolutionary performance by dynamically utilizing the most effective operator at different stages of evolution. The proposed algorithms are tested on a set of 21 benchmark problems. The results show that the new mixed strategies perform equally well or better than the best of the previous evolutionary methods for all of the benchmark problems. The proposed MMCGA has shown significant superiority over others.

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Co-Evolutionary Algorithms Based on Mixed Strategy

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