MOEA Test Suites

  • 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

Why test MOEAs? To evaluate, compare, classify, and improve algorithm performance (effectiveness and efficiency)! What is an MOEA test? An MOP test function, an MOP test suite, pedagogical functions, or a real-world problem? How to find an appropriate MOEA test? MOEA literature, historical use, test generators, or well known real-world applications. When to test? Incremental algorithm and test development starting early or wait until end of development. How to design an MOEA test? Assumptions, computational platform selection, statistical tools, metric selection, experimental plan, and an on-going process. Therefore, considerable effort must be expended not only to define proper MOP tests and generate the proper design of MOEA experiments, but also to employ the honest selection of appropriate testing metrics and associated statistical evaluation and comparison. In this chapter, the development of various MOP test suites is addressed, and in the next chapter, their use in appropriate MOEA evaluations is discussed.

Keywords

Decision Variable Pareto Front Test Suite Objective Space Side Constraint 
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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