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The Effects of Representational Bias on Collaboration Methods in Cooperative Coevolution

  • R. Paul Wiegand
  • William C. Liles
  • Kenneth A. De Jong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

Cooperative coevolutionary algorithms (CCEAs) have been applied to many optimization problems with varied success. Recent empirical studies have shown that choices surrounding methods of collaboration may have a strong impact on the success of the algorithm. Moreover, certain properties of the problem landscape, such as variable interaction, greatly influence how these choices should be made. A more general view of variable interaction is one that considers epistatic linkages which span population boundaries. Such linkages can be caused by the decomposition of the actual problem, as well as by CCEA representation decisions regarding population structure. We posit that it is the way in which represented problem components interact, and not necessarily the existence of cross-population epistatic linkages that impacts these decisions. In order to explore this issue, we identify two different kinds of representational bias with respect to the population structure of the algorithm, decompositional bias and linkage bias. We provide analysis and constructive examples which help illustrate that even when the algorithm’s representation is poorly suited for the problem, the choice of how best to select collaborators can be unaffected.

Keywords

Epistatic Interaction Simple Genetic Algorithm Travel Salesperson Problem Cooperative Coevolution Coevolutionary Algorithm 
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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References

  1. 1.
    M. Potter and K. De Jong. A cooperative coevolutionary approach to function optimization. In Y. Davidor and H.-P. Schwefel, editors, Proceedings of the Third International Conference on Parallel Problem Solving from Nature (PPSN III), pages 249–257. Springer-Verlag, 1994.Google Scholar
  2. 2.
    M. Potter. The Design and Analysis of a Computational Model of Cooperative CoEvolution. PhD thesis, George Mason University, Fairfax, Virginia, 1997.Google Scholar
  3. 3.
    L. Bull. Evolutionary computing in multi-agent environments: Partners. In Thomas Baeck, editor, Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA), pages 370–377. Morgan Kaufmann, 1997.Google Scholar
  4. 4.
    R. Paul Wiegand, William Liles, and Kenneth De Jong. An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In Spector [15], pages 1235–1242.Google Scholar
  5. 5.
    R. Watson and J. Pollack. Coevolutionary dynamics in a minimal substrate. In Spector [15], pages 702–709.Google Scholar
  6. 6.
    S. Ficici and J. Pollack. Challenges in coevolutionary learning: Arms-race dynamics, open-endedness, and mediocre stable states. In Adami et al, editor, Proceedings of the Sixth International Conference on Artificial Life, pages 238–247, Cambridge, MA, 1998. MIT Press.Google Scholar
  7. 7.
    D. Cli. and G. F. Miller. Tracking the red queen: Measurements of adaptive progress in co-evolutionary sumulations. In Proceedings of the Third European Conference on Artificial Life, pages 200–218. Springer-Verlag, 1995.Google Scholar
  8. 8.
    M. Vose. The Simple Genetic Algorithm. MIT Press, 1999.Google Scholar
  9. 9.
    S. Ficici and J. Pollack. A game-theoretic approach to the simple coevolutionary algorithm. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, editors, Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI), pages 467–476. Springer-Verlag, 2000.Google Scholar
  10. 10.
    R. Paul Wiegand, William Liles, and Kenneth De Jong. Analyzing cooperative coevolution with evolutionary game theory. In D. Fogel, editor, Proceedings of CEC 2002. IEEE Press, 2002. (To appear).Google Scholar
  11. 11.
    J. Paredis. Coevolutionary computation. Artificial Life Journal, 2(3), 1996.Google Scholar
  12. 12.
    L. Bull. On coevolutionary genetic algorithms. Soft Computing, 5:201–207, 2001.zbMATHCrossRefGoogle Scholar
  13. 13.
    R. Salomon. Performance degradation of genetic algorithms under coordinate rotation. In L. Fogel, P. Angeline, and T. Bäck, editors, Proceedings of the Fifth Annual Conference on Evolutionary Programming, pages 153–161. MIT Press, 1996.Google Scholar
  14. 14.
    Yuval Davidor. Epistasis variance: A viewpoint on ga-hardness. In G. Rawlins, editor, Foundations of Genetic Algorithms (FOGA), pages 23–35. Morgan Kaufmann, 1990.Google Scholar
  15. 15.
    L. Spector, editor. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2001. Morgan Kaufmann, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • R. Paul Wiegand
    • 1
  • William C. Liles
    • 1
  • Kenneth A. De Jong
    • 1
  1. 1.George Mason UniversityFairfaxUSA

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