A study on the effect of multi-parent recombination in real coded genetic algorithms

We investigate real coded genetic algorithms in which more than two parents are involved in recombination operation. We propose three types of multi-parent recombination operators; the center of mass crossover (CMX), multi-parent feature-wise crossover (MFX), and seed crossover (SX). Each of these operators is a natural generalization of 2-parent recombination operator. These operators are evaluated on several test functions. The results showed clearly that multi-parent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.

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

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

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

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

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

[6]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[7]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

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

[9]  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 .

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

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

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

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

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

[15]  David W. Corne,et al.  A Real Coded Genetic Algorithm with an Explorer and an Exploiter Populations , 1997, ICGA.

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

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

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

[19]  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.

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

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

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

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