Evolutionary Algorithm MOP Approaches

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

Abstract

Both researchers and practitioners certainly have a strong interest in knowing the state-of-the-art of a discipline in which they are interested to work. For researchers, this is the normal procedure to trigger original contributions. For practitioners, this knowledge of the area allows them to choose the most appropriate algorithm(s) for their specific application.

Keywords

Pareto Front Multiobjective Optimization Nondominated Solution Binary Tournament Selection Pareto Ranking 
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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