A Solution to the Challenge of Optimization on "Golf-Course"-Like Fitness Landscapes
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Publication Details
Output type: Journal article
Author list: Melo HPM, Franks A, Moreira AA, Diermeier D, Andrade JS, Amaral LAN
Publisher: Public Library of Science
Publication year: 2013
Journal: PLoS ONE (1932-6203)
Journal acronym: PLOS ONE
Volume number: 8
Issue number: 11
ISSN: 1932-6203
eISSN: 1932-6203
Languages: English-Great Britain (EN-GB)
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Open access status: gold
Full text URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0078401&type=printable
Abstract
Genetic algorithms (GAs) have been used to find efficient solutions to numerous fundamental and applied problems. While GAs are a robust and flexible approach to solve complex problems, there are some situations under which they perform poorly. Here, we introduce a genetic algorithm approach that is able to solve complex tasks plagued by so-called ''golf-course''-like fitness landscapes. Our approach, which we denote variable environment genetic algorithms (VEGAs), is able to find highly efficient solutions by inducing environmental changes that require more complex solutions and thus creating an evolutionary drive. Using the density classification task, a paradigmatic computer science problem, as a case study, we show that more complex rules that preserve information about the solution to simpler tasks can adapt to more challenging environments. Interestingly, we find that conservative strategies, which have a bias toward the current state, evolve naturally as a highly efficient solution to the density classification task under noisy conditions.
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