A Game-Theoretic Approach for Designing Mixed Mutation Strategies

  • Jun He
  • Xin Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3612)

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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References

  1. 1.
    Fogel, D.: Evolution Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (1995)Google Scholar
  2. 2.
    Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evolutionary Computation 3(2), 82–102 (1999)CrossRefGoogle Scholar
  3. 3.
    Lee, C.-Y., Yao, X.: Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans. on Evolutionary Computation 8(2), 1–13 (2004)CrossRefGoogle Scholar
  4. 4.
    Wolpert, D.H., Macready, W.G.: No free lunch theorem for optimization. IEEE Trans. on Evolutionary Computation 1(1), 67–82 (1997)CrossRefGoogle Scholar
  5. 5.
    Chellapilla, K.: Combining mutation operators in evolutionary programming. IEEE Trans. on Evolutionary Computation 2(3), 91–96 (1998)CrossRefGoogle Scholar
  6. 6.
    Weibull, J.W.: Evolutionary Game Theory. MIT Press, Cambridge (1995)MATHGoogle Scholar
  7. 7.
    Dutta, P.K.: Strategies and Games. The MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Ficici, S.G., Melnik, O., Pollack, J.B.: A game-theoretic investigation of selection methods used in evolutionary algorithms. In: Proc. of 2000 Congress on Evolutionary Computation, pp. 880–887. IEEE Press, Los Alamitos (2000)Google Scholar
  9. 9.
    Wiegand, R.P., Liles, W.C., De Jong, K.A.: Analyzing coperative coevolution with evolutionary game theory. In: Proc. of 2002 Congress on Evolutionary Computation, pp. 1600–1605. IEEE Press, Los Alamitos (2002)Google Scholar
  10. 10.
    Ficici, S.G., Pollack, J.B.: A game-theoretic memory mechanism for coevolution. In: Proc. of 2003 Genetic and Evolutionary Computation Conference, pp. 286–297. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Liang, K.-H., Yao, X., Newton, C.S.: Adapting self-adaptive parameters in evolutionary algorithms. Applied Intellegence 15(3), 171–180 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Fogel, D., Fogel, G., Ohkura, K.: Multiple-vector self-adaptation in evolutionary algorithms. BioSystems 61, 155–162 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jun He
    • 1
    • 2
  • Xin Yao
    • 1
  1. 1.School of Computer ScienceThe University of BirminghamEdgbaston, BirminghamU.K.
  2. 2.Department of Computer ScienceBeijing Jiaotong UniversityBeijingChina

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