Evolutionary Multi-objective Optimization in Uncertain Environments - Issues and Algorithms (Paperback, Softcover reprint of hardcover 1st ed. 2009)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Computational Intelligence, 186
Release date:	October 2010
First published:	2009
Authors:	Chi-Keong Goh • Kay Chen Tan
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Paperback
Pages:	271
Edition:	Softcover reprint of hardcover 1st ed. 2009
ISBN-13:	978-3-642-10113-7
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Professional & Technical > Technology: general issues > Technical design > Computer aided design (CAD) Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-642-10113-5
Barcode:	9783642101137

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