Fitness Uniform Selection to Preserve Genetic Diversity

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dc.creatorHutter, Marcus
dc.date2001-03-14
dc.date.accessioned2026-07-07T03:17:01Z
dc.date.available2026-07-07T03:17:01Z
dc.descriptionIn evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other. We propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure towards sparsely populated fitness regions, not necessarily towards higher fitness, as is the case for all other selection schemes. We show that the new selection scheme can be much more effective than standard selection schemes.
dc.description13 LaTeX pages, 1 eps figure
dc.identifierhttps://arxiv.org/abs/cs/0103015
dc.identifierhttp://arxiv.org/abs/cs/0103015
dc.identifierProceedings of the 2002 Congress on Evolutionary Computation (CEC-2002) 783-788
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30569
dc.subjectArtificial Intelligence
dc.subjectDistributed, Parallel, and Cluster Computing
dc.subjectMachine Learning
dc.subjectQuantitative Biology
dc.subjectI.2; I.2.6; I.2.8; F.2
dc.titleFitness Uniform Selection to Preserve Genetic Diversity
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