Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms / Edition 1

Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms / Edition 1

by Chi-Keong Goh, Kay Chen Tan
     
 

ISBN-10: 3540959750

ISBN-13: 9783540959755

Pub. Date: 03/06/2009

Publisher: Springer Berlin Heidelberg

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

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Overview

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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Product Details

ISBN-13:
9783540959755
Publisher:
Springer Berlin Heidelberg
Publication date:
03/06/2009
Series:
Studies in Computational Intelligence Series, #186
Edition description:
2009
Pages:
271
Product dimensions:
6.40(w) x 9.30(h) x 0.90(d)

Table of Contents

Introduction.- A Distributed Cooperative Coevolutionary Algorithm for MO Optimization.- Hybrid Multi-objective Evolutionary Design for Neural Networks.- An Investigation on Noise-Induced Features in Robust Evolutionary Multi-Objective Optimization.- Conclusions.

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