New Approaches to Coevolutionary Worst-Case Optimization

  • Jürgen Branke
  • Johanna Rosenbusch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


Many real-world optimization problems involve uncertainty. In this paper, we consider the case of worst-case optimization, i.e., the user is interested in a solution’s performance in the worst case only. If the number of possible scenarios is large, it is an optimization problem by itself to determine a solution’s worst case performance. In this paper, we apply coevolutionary algorithms to co-evolve the worst case test cases along with the solution candidates. We propose a number of new variants of coevolutionary algorithms, and show that these techniques outperform previously proposed coevolutionary worst-case optimizers on some simple test problems.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jürgen Branke
    • 1
  • Johanna Rosenbusch
    • 1
  1. 1.Institute AIFBUniversity of KarlsruheGermany

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