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dc.contributor.authorGeiger, Peter C.
dc.date.accessioned2015-10-09T04:30:21Z
dc.date.available2015-10-09T04:30:21Z
dc.date.issued2015-05
dc.identifier.othergeiger_peter_c_201505_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/geiger_peter_c_201505_ms
dc.identifier.urihttp://hdl.handle.net/10724/32903
dc.description.abstractMany real world problems are too complex to solve with traditional programming methods in a reasonable amount of time. Stochastic optimization techniques have been applied to this class of problems with success. Set up and tuning an algorithm can be a daunting task, so this thesis first presents a method of simple optimization requiring no tuning parameters. Then, methods for dealing with search spaces with invalid solution space are introduced and compared.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectOptimization
dc.subjectGenetic Algorithm
dc.subjectSwarm Intelligence
dc.subjectRepair Operators
dc.titleA comparison of novel stochastic optimization methods
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorWalter D. Potter
dc.description.committeeWalter D. Potter
dc.description.committeePete Bettinger


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