• Carlos A. Coello Coello
  • David A. Van Veldhuizen
  • Gary B. Lamont
Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 5)


The intent of this monograph as indicated in the Preface is to provide as complete as possible a foundation for the development and use of multiobjective evolutionary algorithms. The previous chapters have provided a comprehensive framework for the study and extension of such stochastic search algorithms; the general goal being to generate the Pareto optimal front with a uniform density of points on the front along with the values of the associated decision variables. The classified variety of proposed MOEAs is presented in Chapter 2 along with their subjective advantages and disadvantages. Various generic test suites are presented in Chapter 3 across unconstrained numerical problems, constrained numerical problems, N P-complete problems and real-world applications. Viable metrics are presented in Chapter 4 to evaluate each proposed MOEA across a variety of evolutionary operators and performance visualizations for test suite functions. As in all chapters, areas of proposed research are presented at the end of each chapter along with MOEA discussion questions for the student as well as the practioner.


Pareto Front Pareto Optimal Front Multiobjective Evolutionary Algorithm Pareto Ranking Stochastic Search Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2002

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

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