Evolutionary programming using a mixed mutation strategy

Abstract Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several mutation operators into a single algorithm, can overcome this problem. Inspired by evolutionary game theory, this paper presents a mixed strategy evolutionary programming algorithm that employs the Gaussian, Cauchy, Levy, and single-point mutation operators. The novel algorithm is tested on a set of 22 benchmark problems. The results show that the mixed strategy performs equally well or better than the best of the four pure strategies does, for all of the benchmark problems.

[1]  Kumar Chellapilla,et al.  Combining mutation operators in evolutionary programming , 1998, IEEE Trans. Evol. Comput..

[2]  Huanwen Tang,et al.  A single-point mutation evolutionary programming , 2004, Inf. Process. Lett..

[3]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[6]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[7]  Se-Young Oh,et al.  A new mutation rule for evolutionary programming motivated from backpropagation learning , 2000, IEEE Trans. Evol. Comput..

[8]  Masao Iwamatsu,et al.  Generalized evolutionary programming with Lévy-type mutation , 2002 .

[9]  Thomas Bck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[10]  Xin Yao,et al.  Evolutionary programming using mutations based on the Levy probability distribution , 2004, IEEE Transactions on Evolutionary Computation.

[11]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[12]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[13]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[16]  Jörgen W. Weibull,et al.  Evolutionary Game Theory , 1996 .

[17]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[18]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[19]  Alan S. Morris,et al.  Performance improvement of self-adaptive evolutionary methods with a dynamic lower bound , 2002, Inf. Process. Lett..

[20]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[21]  Xin Yao,et al.  A Game-Theoretic Approach for Designing Mixed Mutation Strategies , 2005, ICNC.

[22]  David B. Fogel,et al.  Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence) , 2006 .