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Evolutionary Algorithms for Solving Multi-Objective Problems

  • Book
  • © 2002

Overview

Authors:
  1. Carlos A. Coello Coello

    1. CINVESTAV-IPN, Mexico, Mexico

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    You can also search for this author in PubMed   Google Scholar

  2. David A. Veldhuizen

    1. Air Force Research Laboratory, Brooks Air Force Base, USA

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    You can also search for this author in PubMed   Google Scholar

  3. Gary B. Lamont

    1. Air Force Institute of Technology, Dayton, USA

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    You can also search for this author in PubMed   Google Scholar

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

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xxxv
    Download chapter PDF

  2. Basic Concepts

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 1-57

  3. Evolutionary Algorithm MOP Approaches

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 59-99

  4. MOEA Test Suites

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 101-140

  5. MOEA Testing and Analysis

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 141-178

  6. MOEA Theory and Issues

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 179-205

  7. Applications

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 207-292

  8. MOEA Parallelization

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 293-320

  9. Multi-Criteria Decision Making

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 321-347

  10. Special Topics

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 349-388

  11. Epilog

    • Carlos A. Coello Coello, David A. Van Veldhuizen, Gary B. Lamont
    Pages 389-391

  12. Back Matter

    Pages 393-576
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Keywords

About this book

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.

Authors and Affiliations

  • CINVESTAV-IPN, Mexico, Mexico

    Carlos A. Coello Coello

  • Air Force Research Laboratory, Brooks Air Force Base, USA

    David A. Veldhuizen

  • Air Force Institute of Technology, Dayton, USA

    Gary B. Lamont

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