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dc.contributor.authorZhang, Chongshan
dc.date.accessioned2014-03-03T23:19:46Z
dc.date.available2014-03-03T23:19:46Z
dc.date.issued2005-05
dc.identifier.otherzhang_chongshan_200505_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200505_ms
dc.identifier.urihttp://hdl.handle.net/10724/22557
dc.description.abstractIn this thesis, we present two techniques to improve the performance of the genetic algorithm (GA). First we use Maximal Hyper-Rectangle (MHR) analysis to improve GA search reliability. We propose a method to find a sufficiently large MHR for new individual insertion in GA with polynomial computational complexity. Second, we propose an idea of relative fitness to improve increasing the convergence and searching more space. The individuals are selected for reproduction according to both of their global rank and regional rank. We apply the two techniques to some GA problems, and the results demonstrate their merits.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectGenetic Algorithms
dc.subjectOptimization
dc.subjectMaximal Hyper-Rectangle
dc.subjectRegional Rank
dc.subjectRelative Fitness.
dc.titleImproving GA performance by using Maximal Hyper-Rectangle analysis and relative fitness
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorKhaled Rasheed
dc.description.committeeKhaled Rasheed
dc.description.committeeLiming Cai
dc.description.committeeThiab Taha


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