A Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations

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dc.contributor.authorGantovnik, Vladimir B.en
dc.contributor.authorGürdal, Zaferen
dc.contributor.authorWatson, Layne T.en
dc.contributor.authorAnderson-Cook, Christine M.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:37:10Zen
dc.date.available2013-06-19T14:37:10Zen
dc.date.issued2003en
dc.description.abstractThis paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation.The additions involve memory as a function of both discrete and continuous design variables, and multivariate approximation of the individual functions' responses in terms of several continuous design variables. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00000667/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000667/01/gaAIAAJ03.pdfen
dc.identifier.trnumberTR-03-22en
dc.identifier.urihttp://hdl.handle.net/10919/20145en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectParallel computationen
dc.titleA Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximationsen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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