On the robustness of evolving populations

Tobias Friedrich, Timo Kötzing, Andrew M. Sutton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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 languageEnglish (US)
Title of host publicationParallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings
EditorsEmma Hart, Ben Paechter, Julia Handl, Manuel López-Ibáñez, Peter R. Lewis, Gabriela Ochoa
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319458229
StatePublished - 2016
Event14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 - Edinburgh, United Kingdom
Duration: Sep 17 2016Sep 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9921 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Parallel Problem Solving from Nature, PPSN 2016
Country/TerritoryUnited Kingdom

Bibliographical note

Funding 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).

Publisher Copyright:
© Springer International Publishing AG 2016.


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