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Evolutionary Algorithms for Solving Multi-Objective Problems

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

Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 5)

Table of contents

  1. Front Matter
    Pages i-xxxv
  2. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 1-57
  3. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 59-99
  4. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 101-140
  5. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 141-178
  6. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 179-205
  7. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 207-292
  8. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 293-320
  9. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 321-347
  10. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 349-388
  11. Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 389-391
  12. Back Matter
    Pages 393-576

About this book

Introduction

Researchers and practitioners alike are increasingly turning to search, op­ timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solv­ ing problems and inventing new hardware and software that rival human designs. The Kluwer Series on Genetic Algorithms and Evolutionary Computation pub­ lishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implemen­ tation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also pub­ lishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described. This encyclopedic volume on the use of the algorithms of genetic and evolu­ tionary computation for the solution of multi-objective problems is a landmark addition to the literature that comes just in the nick of time. Multi-objective evolutionary algorithms (MOEAs) are receiving increasing and unprecedented attention. Researchers and practitioners are finding an irresistible match be­ tween the popUlation available in most genetic and evolutionary algorithms and the need in multi-objective problems to approximate the Pareto trade-off curve or surface.

Keywords

algorithms chemistry classification computation computer computer science ecology evolution evolutionary algorithm information operations research optimization physics science search algorithm

Authors and affiliations

  • Carlos A. Coello Coello
    • 1
  • David A. Van Veldhuizen
    • 2
  • Gary B. Lamont
    • 3
  1. 1.CINVESTAV-IPNMexicoMexico
  2. 2.Air Force Research LaboratoryBrooks Air Force BaseUSA
  3. 3.Air Force Institute of TechnologyDaytonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-5184-0
  • Copyright Information Springer-Verlag US 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-5186-4
  • Online ISBN 978-1-4757-5184-0
  • Series Print ISSN 1568-2587
  • Buy this book on publisher's site
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