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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fogel, D.: Evolution Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (1995)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evolutionary Computation 3(2), 82–102 (1999)
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)
Wolpert, D.H., Macready, W.G.: No free lunch theorem for optimization. IEEE Trans. on Evolutionary Computation 1(1), 67–82 (1997)
Chellapilla, K.: Combining mutation operators in evolutionary programming. IEEE Trans. on Evolutionary Computation 2(3), 91–96 (1998)
Weibull, J.W.: Evolutionary Game Theory. MIT Press, Cambridge (1995)
Dutta, P.K.: Strategies and Games. The MIT Press, Cambridge (1999)
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)
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)
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)
Liang, K.-H., Yao, X., Newton, C.S.: Adapting self-adaptive parameters in evolutionary algorithms. Applied Intellegence 15(3), 171–180 (2001)
Fogel, D., Fogel, G., Ohkura, K.: Multiple-vector self-adaptation in evolutionary algorithms. BioSystems 61, 155–162 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11539902_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)