On Test Functions for Evolutionary Multi-objective Optimization

  • Tatsuya Okabe
  • Yaochu Jin
  • Markus Olhofer
  • Bernhard Sendhoff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

In order to evaluate the relative performance of optimization algorithms benchmark problems are frequently used. In the case of multi-objective optimization (MOO), we will show in this paper that most known benchmark problems belong to a constrained class of functions with piecewise linear Pareto fronts in the parameter space. We present a straightforward way to define benchmark problems with an arbitrary Pareto front both in the fitness and parameter spaces. Furthermore, we introduce a difficulty measure based on the mapping of probability density functions from parameter to fitness space. Finally, we evaluate two MOO algorithms for new benchmark problems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tatsuya Okabe
    • 1
  • Yaochu Jin
    • 1
  • Markus Olhofer
    • 1
  • Bernhard Sendhoff
    • 1
  1. 1.Honda Research Institute Europe GmbHOffenbach/MainGermany

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