Abstract
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.
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References
Goldberg, D.: Genetic Algorithm in Search Optimization and Machine Learning, Fourth Impression. Pearson Education (2009)
Zbigniew, M.: Genetic algorithms + data structures = evolution programs. Springer Science & Business Media (2013)
Gonzalez, T.F.: Handbook of Approximation Algorithms and Metaheuristics. CRC Press (2007)
Mitchell, M.: An introduction to genetic algorithms. MIT Press (1998)
Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithm. Springer Science & Business Media (2007)
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybernet. 24, 656–667 (1994)
Picek, S., Golub, M., Jakobovic, D.: Evaluation of crossover operator performance in genetic algorithms with binary representation. Bio-Insp. Comput. Appl. 223–230 (2012)
Sheskin, D.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th ed. Chapman and Hall/CRC (2007)
Spears, W., Vic, A.: A study of crossover operators in genetic programming. Methodol. Intell. Syst. 409–418 (1991)
Poon, P.W., Carter, J.N.: Genetic algorithm crossover operators for ordering applications. Comput. Oper. Res. 22, 135–147 (1995)
Eshelman, L.J.: The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In: Foundations of Genetic Algorithms, pp. 265–283. Morgan Kaufmann, San Francisco, CA, USA (1991)
Dumitrescu, D., Lazzerini, B., Jain, L.C., Dumitrescu, A.: Evolutionary Computation. CRC Press, Florida, USA (2000)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Maintaining the diversity of solutions by non-geometric binary crossover: a worst one-max solver competition case study. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO’08. pp. 1111–1112 (2008)
Chan, K.Y., Kwong, C.K., Jiang, H., Aydin, M.E., Fogarty, T.C.: A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design. Expert Syst. Appl. 37, 3853–3862 (2010)
Tsai, J.T., Liu, T.K., Chou, J.H.: Hybrid taguchi-genetic algorithm for global numerical optimization. IEEE Trans. Evol. Comput. 8(4), 365–377 (2004)
Leung, Y.W., Yuping, W.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans. Evol. Comput. 5, 41–53 (2001)
Digalakis, J.G., Konstantinos, G.M.: An experimental study of benchmarking functions for genetic algorithms. Int. J. Comput. Math. 79, 403–416 (2002)
Pohlheim, H.: Geatbx Examples of Objective Functions (2006). http://www.geatbx.com/download/GEATbx_ObjFunExpl_v37.pdf
Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, Computational Intelligence Laboratory (2013)
Liang, J.J., Qu B.Y., Suganthan P.N., Chen, Q.: Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report 201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore (2014)
Beheshti, Z., Shamsuddin, S.M., Shafaatunnur, H.: Memetic binary particle swarm optimization for discrete optimization problems. Inf. Sci. 299, 58–84 (2015)
Derrac, J., Garcia, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolution. Computat. 1, 3–18 (2011)
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Jain, A., Mishra, T., Grover, J., Verma, V., Srivastava, S. (2019). Performance Assessment of Thirteen Crossover Operators Using GA. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_60
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DOI: https://doi.org/10.1007/978-981-13-1595-4_60
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