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A Game-Theoretic Approach for Designing Mixed Mutation Strategies

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

Different mutation operators have been proposed in evolutionary programming. However, each operator may be efficient in solving a subset of problems, but will fail in another one. Through a mixture of various mutation operators, it is possible to integrate their advantages together. This paper presents a game-theoretic approach for designing evolutionary programming with a mixed mutation strategy. The approach is applied to design a mixed strategy using Gaussian and Cauchy mutations. The experimental results show the mixed strategy can obtain the same performance as, or even better than the best of pure strategies.

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Authors and Affiliations

  1. School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.

    Jun He & Xin Yao

  2. Department of Computer Science, Beijing Jiaotong University, Beijing, China

    Jun He

Authors
  1. Jun He

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  2. Xin Yao

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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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He, J., Yao, X. (2005). A Game-Theoretic Approach for Designing Mixed Mutation Strategies. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_33

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