Evolutionary Algorithms for Solving Multi-Objective Problems

Second Edition

  • Carlos A. Coello Coello
  • Gary B. Lamont
  • David A. Van Veldhuizen

Part of the Genetic and Evolutionary Computation Series book series (GEVO)

Table of contents

  1. Front Matter
    Pages I-XXI
  2. Pages 1-60
  3. Pages 175-232
  4. Pages 283-337
  5. Pages 339-441
  6. Pages 443-513
  7. Back Matter
    Pages 623-800

About this book

Introduction

This textbook is the second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly augmented with contemporary knowledge and adapted for the classroom. All the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and student-friendly fashion, incorporating state-of-the-art research results. The diversity of serial and parallel MOEA structures are given, evaluated and compared. The book provides detailed insight into the application of MOEA techniques to an array of practical problems. The assortment of test suites are discussed along with the variety of appropriate metrics and relevant statistical performance techniques.

Distinctive features of the new edition include:

  • Designed for graduate courses on Evolutionary Multi-Objective Optimization, with exercises and links to a complete set of teaching material including tutorials

  • Updated and expanded MOEA exercises, discussion questions and research ideas at the end of each chapter

  • New chapter devoted to coevolutionary and memetic MOEAs with added material on solving constrained multi-objective problems

  • Additional material on the most recent MOEA test functions and performance measures, as well as on the latest developments on the theoretical foundations of MOEAs

  • An exhaustive index and bibliography

This self-contained reference is invaluable to students, researchers and in particular to computer scientists, operational research scientists and engineers working in evolutionary computation, genetic algorithms and artificial intelligence.

 

"...If you still do not know this book, then, I urge you to run-don't walk-to your nearest on-line or off-line book purveyor and click, signal or otherwise buy this important addition to our literature."

-David E. Goldberg, University of Illinois at Urbana-Champaign

Keywords

Algorithms Analysis algorithm classification computer computer science evolutionary algorithm heuristics metaheuristic optimization

Authors and affiliations

  • Carlos A. Coello Coello
    • 1
  • Gary B. Lamont
    • 2
  • David A. Van Veldhuizen
    • 3
  1. 1.Depto. de ComputaciónCINVESTAV-IPNCol. San Pedro ZacatencoMéxico
  2. 2.Department of Electrical and Computer EngineeringGraduate School of Engineering Air Force Institute of Technology45433-7765DaytonUSA
  3. 3.HQQ AMC/A962225-5307Scott AFBUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-36797-2
  • Copyright Information Springer US 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-33254-3
  • Online ISBN 978-0-387-36797-2
  • Series Print ISSN 1932-0167
  • About this book