Fitness landscapes and evolvability

Evolutionary Computation
Tom SmithMichael O'Shea

Abstract

In this paper, we develop techniques based on evolvability statistics of the fitness landscape surrounding sampled solutions. Averaging the measures over a sample of equal fitness solutions allows us to build up fitness evolvability portraits of the fitness landscape, which we show can be used to compare both the ruggedness and neutrality in a set of tunably rugged and tunably neutral landscapes. We further show that the techniques can be used with solution samples collected through both random sampling of the landscapes and online sampling during optimization. Finally, we apply the techniques to two real evolutionary electronics search spaces and highlight differences between the two search spaces, comparing with the time taken to find good solutions through search.

References

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Citations

Aug 21, 2003·Journal of Theoretical Biology·Akira Sasaki, Martin A Nowak
Apr 12, 2003·Bio Systems·Tom SmithMichael O'Shea
Nov 15, 2011·Molecular Biology and Evolution·Xin HeSaurabh Sinha
May 26, 2004·Evolutionary Computation·John Cartlidge, Seth Bullock
Nov 22, 2008·PLoS Computational Biology·Andreea Munteanu, Ricard V Solé
Dec 20, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Xiao-Feng Xie, Jiming Liu
Oct 29, 2014·Computational and Structural Biotechnology Journal·Daphne EzerBoris Adryan
Sep 12, 2014·PLoS Computational Biology·Krishnendu ChatterjeeMartin A Nowak
Nov 9, 2016·Artificial Life·Thomas LaBarChristoph Adami
Nov 27, 2018·Evolutionary Computation·Pascal KerschkeHeike Trautmann
Mar 6, 2013·Molecular BioSystems·Alexander E LobkovskyEugene V Koonin

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