Evolutionary computing for spacecraft power subsystem design search and optimization

Kordon, Mark; Klimick, Gerhard; Hanks, David; Hua, Hook

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