Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms

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dc.creatorSastry, Kumara
dc.creatorPelikan, Martin
dc.creatorGoldberg, David E.
dc.date2005-02-12
dc.date.accessioned2026-07-07T03:22:31Z
dc.date.available2026-07-07T03:22:31Z
dc.descriptionThe paper analyzes the scalability of multiobjective estimation of distribution algorithms (MOEDAs) on a class of boundedly-difficult additively-separable multiobjective optimization problems. The paper illustrates that even if the linkage is correctly identified, massive multimodality of the search problems can easily overwhelm the nicher and lead to exponential scale-up. Facetwise models are subsequently used to propose a growth rate of the number of differing substructures between the two objectives to avoid the niching method from being overwhelmed and lead to polynomial scalability of MOEDAs.
dc.descriptionSubmitted to Genetic and Evolutionary Computation Conference, GECCO-2005
dc.identifierhttps://arxiv.org/abs/cs/0502057
dc.identifierhttp://arxiv.org/abs/cs/0502057
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32629
dc.subjectNeural and Evolutionary Computing
dc.subjectArtificial Intelligence
dc.titleDecomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms
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