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Evolutionary computing for spacecraft power subsystem design search and optimization

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dc.contributor.author Kordon, Mark
dc.contributor.author Klimick, Gerhard
dc.contributor.author Hanks, David
dc.contributor.author Hua, Hook
dc.date.accessioned 2006-01-27T18:24:22Z
dc.date.available 2006-01-27T18:24:22Z
dc.date.issued 2004-03-10
dc.identifier.citation 2004 IEEE Aerospace Conference, Big Sky, Montana, March 10, 2004. en
dc.identifier.clearanceno 04-0708
dc.identifier.uri http://hdl.handle.net/2014/38392
dc.description.abstract Multi-objective optimization involves finding one or more optimal solutions when there is more than one conflicting objective. This means that a solution that is better in one objective compromises or trades-off, other objectives. Trade Studies are conducted by flight projects to create mission concepts with different trade-off solutions for mass, cost, performance and risk. en
dc.description.sponsorship NASA/JPL en
dc.format.extent 5255501 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2004 en
dc.subject power en
dc.subject subsystem en
dc.subject optimization en
dc.subject evolutionary computing en
dc.title Evolutionary computing for spacecraft power subsystem design search and optimization en
dc.type Presentation en


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