Multi-parent Recombination in Genetic Algorithms with Search Space Boundary Extension by Mirroring

In previous work, we have investigated real coded genetic algorithms with several types of multi-parent recombination operators and found evidence that multi-parent recombination with center of mass crossover (CMX) seems a good choice for real coded GAs. But CMX does not work well on functions which have their optimum on the corner of the search space. In this paper, we propose a method named boundary extension by mirroring (BEM) to cope with this problem. Applying BEM to CMX, the performance of CMX on the test functions which have their optimum on the corner of the search space was much improved. Further, by applying BEM, we observed clear improvement in performance of two-parent recombination on the functions which have their optimum on the corner of the search space. Thus, we suggest that BEM is a good general technique to improve the efficiency of crossover operators in real-coded GAs for a wide range of functions.

[1]  Shigeyoshi Tsutsui,et al.  A study on the effect of multi-parent recombination in real coded genetic algorithms , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[2]  K. Dejong,et al.  An Analysis Of The Behavior Of A Class Of Genetic Adaptive Systems , 1975 .

[3]  Keith E. Mathias,et al.  Crossover Operator Biases: Exploiting the Population Distribution , 1997, ICGA.

[4]  A. E. Eiben,et al.  Multi-Parent's Niche: n-ary Crossovers on NK-Landscapes , 1996, PPSN.

[5]  A. E. Eiben,et al.  Genetic algorithms with multi-parent recombination , 1994, PPSN.

[6]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[7]  Shigenobu Kobayashi,et al.  A Real-Coded Genetic Algorithm for Function Optimization Using the Unimodal Normal Distribution Crossover , 1999 .

[8]  Kenneth A. De Jong,et al.  Using Problem Generators to Explore the Effects of Epistasis , 1997, ICGA.

[9]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[10]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[11]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[12]  Zbigniew Michalewicz,et al.  An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms , 1991, ICGA.

[13]  H. Muhlenbein,et al.  Gene pool recombination and utilization of covariances for the Breeder Genetic Algorithm , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[14]  A. E. Eiben,et al.  Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms , 1995, ECAL.

[15]  Jim Smith,et al.  Recombination strategy adaptation via evolution of gene linkage , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

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

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

[18]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

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

[20]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[21]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[22]  Larry J. Eshelman,et al.  Foundations of Genetic Algorithms-2 , 1993 .

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

[24]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[25]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[26]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: On the Benefits of Sex the (/, ) Theory , 1995, Evolutionary Computation.