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.
|Original language||English (US)|
|Title of host publication||Parallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings|
|Editors||Emma Hart, Ben Paechter, Julia Handl, Manuel López-Ibáñez, Peter R. Lewis, Gabriela Ochoa|
|Number of pages||11|
|State||Published - 2016|
|Event||14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 - Edinburgh, United Kingdom|
Duration: Sep 17 2016 → Sep 21 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||14th International Conference on Parallel Problem Solving from Nature, PPSN 2016|
|Period||9/17/16 → 9/21/16|
Bibliographical noteFunding Information:
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 618091 (SAGE) and the German Research Foundation (DFG) under grant agreement no. FR 2988 (TOSU).
© Springer International Publishing AG 2016.