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dc.contributor.authorNi, Xiao
dc.date.accessioned2014-03-04T21:59:44Z
dc.date.available2014-03-04T21:59:44Z
dc.date.issued2002-08
dc.identifier.otherni_xiao_200208_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/ni_xiao_200208_ms
dc.identifier.urihttp://hdl.handle.net/10724/29276
dc.description.abstractThis thesis compares different methods used in developing and using dynamic reduced models for design optimization. Several methods have been used for forming functional approximation models with which the GA search is performed more efficiently. The informed operators approach for search speedup is discussed. Another hypothesis-instantiation approach in using reduced models has been tested and compared with the informed operator approach. Chapter one first gives some background knowledge. Chapter two describes three different methods for forming reduced models to speed up genetic-algorithm-based design optimization. Chapter three describes two methods for using reduced models to speed up genetic-algorithm-based design optimization. Chapter four gives the experimental results and discussion. Finally, chapter five serves as a summary of the whole thesis.
dc.languageComparison of methods for developing and using dynamic reduced models for design optimization
dc.publisheruga
dc.rightspublic
dc.subjectGADO
dc.subjectQuickprop
dc.subjectRBF
dc.subjectQuadratic least squares,
dc.subjectGenetic engineering
dc.subjectInformed operators
dc.subjectReduced models
dc.titleComparison of methods for developing and using dynamic reduced models for design optimization
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorKhaled Rasheed
dc.description.committeeKhaled Rasheed
dc.description.committeeRon McClendon
dc.description.committeeCharles Cross


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