Deceptiveness and Neutrality - the ND family of fitness landscapes

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dc.creatorBeaudoin, William
dc.creatorVerel, Sébastien
dc.creatorCollard, Philippe
dc.creatorEscazut, Cathy
dc.date2009-01-23
dc.date.accessioned2026-07-07T12:33:48Z
dc.date.available2026-07-07T12:33:48Z
dc.descriptionWhen a considerable number of mutations have no effects on fitness values, the fitness landscape is said neutral. In order to study the interplay between neutrality, which exists in many real-world applications, and performances of metaheuristics, it is useful to design landscapes which make it possible to tune precisely neutral degree distribution. Even though many neutral landscape models have already been designed, none of them are general enough to create landscapes with specific neutral degree distributions. We propose three steps to design such landscapes: first using an algorithm we construct a landscape whose distribution roughly fits the target one, then we use a simulated annealing heuristic to bring closer the two distributions and finally we affect fitness values to each neutral network. Then using this new family of fitness landscapes we are able to highlight the interplay between deceptiveness and neutrality.
dc.descriptionGenetic And Evolutionary Computation Conference, Seatle : États-Unis d'Amérique (2006)
dc.identifierhttps://arxiv.org/abs/0901.3769
dc.identifierhttp://arxiv.org/abs/0901.3769
dc.identifierdoi:10.1145/1143997.1144091
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/217257
dc.subjectArtificial Intelligence
dc.titleDeceptiveness and Neutrality - the ND family of fitness landscapes
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