Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Studies in Computational Intelligence, 186)

Goh, Chi-Keong, Tan, Kay Chen

ISBN 10: 3642101135 / ISBN 13: 9783642101137

Edité par Springer, 2010

A propos de cet article

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N° de réf. du vendeur ERICA77336421011356

Détails bibliographiques

• Titre: Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Studies in Computational Intelligence, 186)
• Éditeur: Springer
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 3642101135
• ISBN à 13 chiffres: 9783642101137
• Reliure: Paperback
• État de l'article: Like New
• Type de livre: book

A propos de ce titre

Synopsis

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 algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined.

The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.