Performance Assessment of Thirteen Crossover Operators Using GA

Performance of genetic algorithms depends on evolutionary operators, i.e., selection, crossover, and mutation, in general, and on the type of crossover operators, in particular. With constant research going on in the field of evolutionary computation, many crossover operators have come into the light, thus making the systematic comparison of these operators necessary. This paper presents comparison of 13 crossover operators on 20 benchmark problems using genetic algorithm. An exhaustive statistical study shows the supremacy of uniform, reduced surrogate, and single-point crossover operators among others.

[1]  Marin Golub,et al.  Evaluation of Crossover Operator Performance in Genetic Algorithms with Binary Representation , 2011, ICIC.

[2]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[3]  Shafaatunnur Hasan,et al.  Memetic binary particle swarm optimization for discrete optimization problems , 2015, Inf. Sci..

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

[5]  Jakub Marecek,et al.  Handbook of Approximation Algorithms and Metaheuristics , 2010, Comput. J..

[6]  P. W. Poon,et al.  Genetic algorithm crossover operators for ordering applications , 1995, Comput. Oper. Res..

[7]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[8]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[9]  Konstantinos G. Margaritis,et al.  An Experimental Study of Benchmarking Functions for Genetic Algorithms , 2002, Int. J. Comput. Math..

[10]  Hisao Ishibuchi,et al.  Maintaining the diversity of solutions by non-geometric binary crossover: a worst one-max solver competition case study , 2008, GECCO '08.

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

[12]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[13]  Kit Yan Chan,et al.  A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design , 2010, Expert Syst. Appl..

[14]  Tung-Kuan Liu,et al.  Hybrid Taguchi-genetic algorithm for global numerical optimization , 2004, IEEE Transactions on Evolutionary Computation.

[15]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[16]  William M. Spears,et al.  A Study of Crossover Operators in Genetic Programming , 1991, ISMIS.