Initial Study on Handling Constrained Optimization Problems in Learnable Evolution Model

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dc.contributor.authorWojtusiak, Janusz
dc.date.accessioned2006-11-03T18:17:38Z
dc.date.available2006-11-03T18:17:38Z
dc.date.issued2006-07
dc.description.abstractLearnable Evolution Model (LEM) is an evolutionary computation methodology that applies hypothesis formulation and instantiation to create new individuals. Initial study has shown that LEM significantly outperforms standard evolutionary computation methods in terms of evolution length on selected benchmark optimization problems. This paper presents initial results from handling constrained optimization problems in LEM. Constraints are classified as instantiable, which can be handled directly during instantiation process, and general, which cannot be directly instantiated. The latter can be handled by applying three different methods presented in this paper.
dc.description.sponsorshipResearch activities of the Machine Learning and Inference Laboratory are supported by the National Science Foundation Grants No. IIS 9906858 and IIS 0097476.
dc.format.extent1989 bytes
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dc.format.mimetypeapplication/pdf
dc.identifier.citationWojtusiak, J., "Initial Study on Handling Constrained Optimization Problems in Learnable Evolution Model," Proceedings of The Graduate Student Workshop at Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, WA, July 8-12, 2006.
dc.identifier.urihttps://hdl.handle.net/1920/1500
dc.language.isoen_US
dc.relation.ispartofseriesP 06-6
dc.subjectConcept learning
dc.subjectInduction
dc.subjectConstrained optimization
dc.titleInitial Study on Handling Constrained Optimization Problems in Learnable Evolution Model
dc.typePresentation

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