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.

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References

  1. 1.
    Avigad, G., Branke, J.: Worst-case robustness and related decision support. In: Genetic and Evolutionary Computation Conference, ACM Press, New York (to appear)Google Scholar
  2. 2.
    Barbosa, H.J.C.: A coevolutionary genetic algorithm for constrained optimization. In: Congress on Evolutionary Computation, vol. 3, pp. 1605–1611 (1999)Google Scholar
  3. 3.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell (2001)Google Scholar
  4. 4.
    Daum, D.A., Deb, K., Branke, J.: Reliability-based optimization for multiple constraints with evolutionary algorithms. In: Congress on Evolutionary Computation, pp. 911–918. IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  5. 5.
    de Jong, E.: The maxsolve algorithm for coevolution. In: Conference on Genetic and Evolutionary Computation, pp. 483–489. ACM Press, New York (2005)Google Scholar
  6. 6.
    Herrmann, J.W.: A genetic algorithm for minimax optimization problems. In: Congress on Evolutionary Computation, vol. 2, pp. 1099–1103. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  7. 7.
    Hillis, D.W.: Co-evolving parasites improve simulated evolution in an optimization procedure. Physica D 42, 228–234 (1990)CrossRefGoogle Scholar
  8. 8.
    Jensen, M.T.: Finding worst-case flexible schedules using coevolution. In: Spector, L., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 1144–1151. Morgan Kaufmann, San Francisco (2001)Google Scholar
  9. 9.
    Jensen, M.T.: A new look at solving minimax problems with coevolutionary genetic algorithms. Applied Optimization 86, 369–384 (2004)CrossRefGoogle Scholar
  10. 10.
    Korn, R., Steffensen, M.: On worst-case portfolio optimization. SIAM Journal on Control and Optimization 46(6), 2013–2030 (2007)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Luke, S., Wiegand, R.P.: When coevolutionary algorithms exhibit evolutionary dynamics. In: Barry, A.M. (ed.) GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pp. 236–241. AAAI Press, Menlo Park (2002)Google Scholar
  12. 12.
    Ong, Y.-S., Nair, P.B., Lum, K.Y.: Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Transactions on Evolutionary Computation 10(4), 392–404 (2006)CrossRefGoogle Scholar
  13. 13.
    Pagie, L., Hogeweg, P.: Information integration and red queen dynamics in coevolutionary optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1260–1267 (2000)Google Scholar
  14. 14.
    Paredis, J.: Coevolutionary computation. Artificial Life 2(4), 355–375 (1995)CrossRefGoogle Scholar
  15. 15.
    Sebald, A.V., Schlenzig, J.: Minimax design of neural net controllers for highly uncertain plants. IEEE Transactions on Neural Networks 5(1), 73–82 (1994)CrossRefGoogle Scholar

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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