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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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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