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New Approaches to Coevolutionary Worst-Case Optimization

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

Abstract

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.

Keywords

Solution Candidate Greedy Method Coevolutionary Algorithm Local Ranking Selected Test Case 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

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

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