ENGLISH

Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms

By Chi-Keong Goh , Kay Chen Tan (auth.)

Book information

Publisher
Springer-Verlag Berlin Heidelberg
Year
2009
ISBN
9783540959755, 3540959750
DOI
10.1007/978-3-540-95976-2
Open Library ID
OL24486990M
Language
english
Format
PDF
Filesize
8 MB (8864367 bytes)
Series
Studies in Computational Intelligence 186
Edition
1
Pages
271\269
Library
Kingdwarf
Time added
2010-01-07 06:59:20
Appl.Mathematics/Computational Methods of Engineer

Description

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.

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