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On the Robustness of Evolving Populations

  • Tobias Friedrich
  • Timo Kötzing
  • Andrew M. SuttonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)

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 (\(\mu \) + 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.

Keywords

Recombination Rate Fitness Landscape Robust Solution Uniform Crossover Mutational Robustness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

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).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Tobias Friedrich
    • 1
  • Timo Kötzing
    • 1
  • Andrew M. Sutton
    • 1
    Email author
  1. 1.Hasso Plattner InstitutePotsdamGermany

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