Methods for Improving the Design and Performance of Evolutionary Algorithms

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dc.contributor.advisorDe Jong, Kenneth A.
dc.contributor.authorBassett, Jeffrey Kermes
dc.creatorBassett, Jeffrey Kermes
dc.date.accessioned2013-03-29T21:06:32Z
dc.date.available2013-03-29T21:06:32Z
dc.date.issued2012
dc.description.abstractEvolutionary Algorithms (EAs) can be applied to almost any optimization or learning problem by making some simple customizations to the underlying represen- tation and/or reproductive operators. This makes them an appealing choice when facing a new or unusual problem. Unfortunately, while making these changes is often easy, getting a customized EA to operate effectively (i.e. find a good solution quickly) can be much more difficult.
dc.format.extent159 pages
dc.identifier.urihttps://hdl.handle.net/1920/8122
dc.language.isoen
dc.rightsCopyright 2012 Jeffrey Kermes Bassett
dc.subjectComputer science
dc.subjectCustomization
dc.subjectEvolutionary computation
dc.subjectGenetic programming
dc.subjectHeritability
dc.subjectPrice's theorem
dc.subjectQuantitative genetics
dc.titleMethods for Improving the Design and Performance of Evolutionary Algorithms
dc.typeDissertation
thesis.degree.disciplineComputer Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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