An adaptive neighboring search using crossover-like mutation for multi modal function optimization

We propose a new population-based evolutionary algorithm which uses a real-coded representation and normal-distribution crossover-like mutation for generating the next searching points. This Gaussian distribution is formed based on the positional relationships between an individual and its neighbors, and is not carried with the self-adapting parameters as an inheritable trait. This algorithm causes the emergence of clusters of individuals within the population, as a result of the evolution of each individual, which does not have any actual intent to cluster. By searching independently, the emergent clusters introduce various solutions that include optima at the same time, even if the problem has strong local minima. The proposed method robustly solves a highly multi-modal 30-dimensional Fletcher-Powell function with a small population size.

[1]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

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

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

[4]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[5]  J. D. Schaffer,et al.  Real-Coded Genetic Algorithms and Interval-Schemata , 1992, FOGA.

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

[7]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[8]  R. Hinterding,et al.  Gaussian mutation and self-adaption for numeric genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[9]  Thomas Bäck,et al.  Evolutionary computation: an overview , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[10]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[11]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[12]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[13]  Zbigniew Michalewicz,et al.  Adaptation in evolutionary computation: a survey , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[14]  Arthur C. Sanderson,et al.  Multimodal Function Optimization Using Minimal Representation Size Clustering and Its Application to Planning Multipaths , 1997, Evolutionary Computation.

[15]  Thomas Bäck,et al.  Empirical Investigation of Multiparent Recombination Operators in Evolution Strategies , 1997, Evolutionary Computation.

[16]  Isao Ono,et al.  A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover , 1997, ICGA.

[17]  S. Kobayashi,et al.  Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[18]  Shigenobu Kobayashi,et al.  A distance alternation model on real-coded genetic algorithms , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[19]  S. Kobayashi,et al.  Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[20]  Hajime Kita,et al.  A Real-Coded Genetic Algorithm using Distance Dependent Alternation Model for Complex Function Optimization , 2000, GECCO.