On the robustness of evolving populations

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Abstract

Most theoretical work that studies the benefit of recombination focuses on the ability of crossover to speed up optimization time on specific search problems. In this paper, we take a slightly different perspective and investigate recombination in the context of evolving solutions that exhibit mutational robustness, i.e., they display insensitivity to small perturbations. Various models in population genetics have demonstrated that increasing the effective recombination rate promotes the evolution of robustness. We show this result also holds in the context of evolutionary computation by rigorously proving crossover promotes the evolution of robust solutions in the standard (μ+1) GA. Surprisingly, our results show that this effect is still present even when robust solutions are at a selective disadvantage due to lower fitness values.

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Friedrich, T., Kötzing, T., & Sutton, A. M. (2016). On the robustness of evolving populations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9921 LNCS, pp. 771–781). Springer Verlag. https://doi.org/10.1007/978-3-319-45823-6_72

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