Abstract
Recent research shows that factorial design methods improve the performance of the crossover operator in evolutionary computation. However the methods employed so far ignore the effects of interaction between genes on fitness, i.e. “epistasis”. Here we propose the application of a systematic method for interaction effect analysis to enhance the performance of the crossover operator. It is shown empirically that the proposed method significantly outperforms existing crossover operators on benchmark problems with high interaction between the variables.
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© 2003 Springer-Verlag Berlin Heidelberg
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Chan, K.Y., Aydin, M.E., Fogarty, T.C. (2003). New Factorial Design Theoretic Crossover Operator for Parametrical Problem. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_3
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DOI: https://doi.org/10.1007/3-540-36599-0_3
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