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A Quantitative Study of Neutrality in GP Boolean Landscapes

Abstract : Neutrality of some boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures some features that are disregarded by uniform random sampling, and we introduce new genetic operators to define the neighborhood of tree structures. We compare the fitness landscape induced by two different sets of functional operators (SNand and SXorNot). The different characteristics of the neutral networks seem to justify the different difficulties of these landscapes for genetic programming.
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Contributor : Sébastien Verel Connect in order to contact the contributor
Submitted on : Monday, July 23, 2007 - 12:30:50 PM
Last modification on : Monday, November 28, 2022 - 5:22:06 PM

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Philippe Collard, Giancarlo Mauri, Yuri Pirola, Marco Tomassini, Leonardo Vanneschi, et al.. A Quantitative Study of Neutrality in GP Boolean Landscapes. Genetic And Evolutionary Computation Conference, Jul 2006, Seatle, United States. pp.895 - 902, ⟨10.1145/1143997.1144152⟩. ⟨hal-00164691⟩



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