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dc.contributor.authorSchmidt, Christian*
dc.date.accessioned2021-02-11T13:12:56Z
dc.date.available2021-02-11T13:12:56Z
dc.date.issued2007*
dc.date.submitted2019-07-30 20:01:58*
dc.identifier34679*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/47148
dc.description.abstractThis book develops efficient methods for the application of Evolutionary Algorithms on stochastic problems. To achieve this, procedures for statistical selection are systematically analyzed with respect to different measures and significantly improved. It is shown how to adapt one of the best procedures for the needs of Evolutionary Algorithms and Evolutionary operators for efficient implementation in stochastic environments are identified.*
dc.languageEnglish*
dc.subjectQA75.5-76.95*
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer scienceen_US
dc.subject.otherEvolutionary Algorithm*
dc.subject.otherUncertainty*
dc.subject.otherSimulation based optimization*
dc.subject.otherBayes*
dc.subject.otherRanking & Selection*
dc.subject.otherGenetic Algorithms*
dc.titleEvolutionary computation in stochastic environments*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000006634*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783866441286*
oapen.pagesVII, 128 p.*
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityScientific or Editorial Board
peerreview.id8ad5c235-9810-49eb-b358-27c8675324d9
peerreview.titleDissertations (Dissertationen)


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