Evolutionary multi-objective optimization in uncertain environments : :: SCD d'Orléans

Evolutionary multi-objective optimization in uncertain environments : issues and algorithms

Evolutionary multi-objective optimization in uncertain environments : issues and algorithms

Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algo...

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書誌詳細
主要な著者:	Goh, Chi-Keong, Tan, Kay Chen (著者)
フォーマット:	Livre numérique
言語:	Anglais
出版事項:	Berlin, Heidelberg : Springer Berlin Heidelberg 2009. Cham : Springer Nature
シリーズ:	Studies in Computational Intelligence 186
オンライン･アクセス:	Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL
注記:	Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale)
Autres localisations:	Voir dans le Sudoc
Edition sous un autre format:	• Evolutionary Multi-objective Optimization in Uncertain Environments, Texte imprimé, 9783540959755 • Evolutionary Multi-objective Optimization in Uncertain Environments, Texte imprimé, 9783642101137 • Evolutionary Multi-objective Optimization in Uncertain Environments, Texte imprimé, 9783642001246

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