Multi-Objective Optimization using Evolutionary Algorithms / Edition 1

Multi-Objective Optimization using Evolutionary Algorithms / Edition 1

by Kalyanmoy Deb
     
 

ISBN-10: 047187339X

ISBN-13: 9780471873396

Pub. Date: 07/12/2001

Publisher: Wiley

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective

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Overview

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.

  • Comprehensive coverage of this growing area of research
  • Carefully introduces each algorithm with examples and in-depth discussion
  • Includes many applications to real-world problems, including engineering design and scheduling
  • Includes discussion of advanced topics and future research
  • Can be used as a course text or for self-study
  • Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms

The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

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Product Details

ISBN-13:
9780471873396
Publisher:
Wiley
Publication date:
07/12/2001
Series:
Wiley Interscience Series in Systems and Optimization Series, #16
Edition description:
1 ED
Pages:
518
Sales rank:
1,009,191
Product dimensions:
6.85(w) x 9.94(h) x 1.36(d)

Table of Contents

Foreword xv

Preface xvii

1 Prologue 1

2 Multi-Objective Optimization 13

3 Classical Methods 49

4 Evolutionary Algorithms 81

5 Non-Elitist Multi-Objective Evolutionary Algorithms 171

6 Elitist Multi-Objective Evolutionary Algorithms 239

7 Constrained Multi-Objective Evolutionary Algorithms 289

8 Salient Issues of Multi-Objective Evolutionary Algorithms 315

9 Applications of Multi-Objective Evolutionary Algorithms 447

10 Epilogue 481

References 489

Index 509

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