Evolutionary Multi-objective Optimization in Uncertain Environments

Issues and Algorithms

  • Chi-Keong Goh
  • Kay Chen Tan

Part of the Studies in Computational Intelligence book series (SCI, volume 186)

Table of contents

  1. Front Matter
  2. Introduction

    1. Chi-Keong Goh, Kay Chen Tan
      Pages 1-40
  3. Part I: Evolving Solution Sets in the Presence of Noise

    1. Front Matter
      Pages 41-41
    2. Chi-Keong Goh, Kay Chen Tan
      Pages 43-54
    3. Chi-Keong Goh, Kay Chen Tan
      Pages 55-99
    4. Chi-Keong Goh, Kay Chen Tan
      Pages 101-121
  4. Part II: Tracking Dynamic Multi-objective Landscapes

    1. Front Matter
      Pages 123-123
    2. Chi-Keong Goh, Kay Chen Tan
      Pages 125-152
    3. Chi-Keong Goh, Kay Chen Tan
      Pages 153-185
  5. Part III: Evolving Robust Solution Sets

    1. Front Matter
      Pages 187-187
    2. Chi-Keong Goh, Kay Chen Tan
      Pages 189-211
    3. Chi-Keong Goh, Kay Chen Tan
      Pages 213-227
    4. Chi-Keong Goh, Kay Chen Tan
      Pages 229-247
    5. Chi-Keong Goh, Kay Chen Tan
      Pages 249-251
  6. Back Matter

About this book


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.


algorithms computer-aided design (CAD) evolution evolutionary algorithm evolutionary computation multi-objective optimization neural network optimization

Authors and affiliations

  • Chi-Keong Goh
    • 1
  • Kay Chen Tan
    • 1
  1. 1.National University of SingaporeSingapore

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-95975-5
  • Online ISBN 978-3-540-95976-2
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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