A Simple Payoff-Based Learning Classifier System

  • Larry Bull
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

It is now ten years since Wilson introduced the ‘Zeroth-level’ learning classifier system with the aim of simplifying Holland’s original system to both aid understanding and improve performance. Despite being comparatively simple, it is still somewhat complex and more recent work has shown the system’s sensitivity to its control parameters, particularly with respect to the underlying fitness sharing process. This paper presents a simple payoff-based learning classifier system with which to explore aspects of fitness sharing in such systems, a further aim being to achieve similar performance to accuracy-based learning classifier systems. The system is described and modelled, before being implemented and tested on the multiplexer task.

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References

  1. 1.
    Holland, J.H.: Adaptation. In: Rosen, R., Snell, F.M. (eds.) Progress in Theoretical Biology, vol. 4, pp. 263–293. Academic Press, London (1976)Google Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  3. 3.
    Holland, J.H.: Adaptive Algorithms for Discovering and using General Patterns in Growing Knowledge Trees. International Journal of Policy Analysis and Information Systems 4(3), 245–268 (1980)Google Scholar
  4. 4.
    Holland, J.H.: Escaping Brittleness. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, vol. 2, pp. 48–78. Morgan Kauffman, San Francisco (1986)Google Scholar
  5. 5.
    Wilson, S.W., Goldberg, D.E.: A Critical Review of Classifier Systems. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 244–255. Morgan Kaufmann, San Francisco (1989)Google Scholar
  6. 6.
    Wilson, S.W.: ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1), 1–18 (1994)CrossRefGoogle Scholar
  7. 7.
    Bull, L., Hurst, J.: ZCS Redux. Evolutionary Computation 10(2), 185–205 (2002)CrossRefGoogle Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
  9. 9.
    Goldberg, D.E., Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimization. In: Grefenstette, J.J. (ed.) Proceedings of the Second International Conference on Genetic Algorithms, pp. 41–49. Lawrence Erlbaum Assoc., Mahwah (1987)Google Scholar
  10. 10.
    Deb, K., Goldberg, D.E.: An Investigation of Niche and Species Formation in Genetic Function Optimization. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 42–50. Morgan Kaufmann, San Francisco (1989)Google Scholar
  11. 11.
    Holland, J.H.: Properties of the Bucket Brigade. In: Grefenstette, J.J. (ed.) Proceedings of the First International Conference on Genetic Algorithms and their Applications, pp. 1–7. Lawrence Erlbaum Associates, Mahwah (1985)Google Scholar
  12. 12.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)Google Scholar
  13. 13.
    Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–177 (1995)CrossRefGoogle Scholar
  14. 14.
    Holland, J.H., Reitman, J.S.: Cognitive Systems based on Adaptive Algorithms. In: Waterman, D.A., Hayes-Roth, F. (eds.) Pattern Directed Inference Systems, pp. 313–329. Academic Press, London (1978)Google Scholar
  15. 15.
    Dorigo, M.: Genetic and Non-Genetic Operators in ALECSYS. Evolutionary Computation 1(2), 151–164 (1993)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Smith, S.F.: A Learning System Based on Genetic Adaptive Algorithms. Ph.D. Dissertation, University of Pittsburgh (1980)Google Scholar
  17. 17.
    Kovacs, T.: Strength or Accuracy? A Comparison of Two Approaches to Fitness Calculation in Learning Classifier Systems. In: Lanzi, P.-L., Stolzmann, W., Wilson, S.W. (eds.) Learning Classifier Systems: From Foundations to Applications, pp. 194–208. Springer, Heidelberg (2000)Google Scholar
  18. 18.
    Wilson, S.W.: Classifier Systems and the Animat Problem. Machine Learning 2, 199–228 (1987)Google Scholar
  19. 19.
    Butz, M., Goldberg, D.E., Lanzi, P.-L.: Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy. Evolutionary Computation 11(3), 239–278 (2003)CrossRefGoogle Scholar
  20. 20.
    Bull, L.: Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 29–36. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  21. 21.
    Deb, K.: Evolutionary Multiobjective Optimization Algorithms. Wiley, Chichester (2001)Google Scholar
  22. 22.
    Bull, L., Studley, M.: Consideration of Multiple Objectives in Neural Learning Classifier Systems. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 558–567. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  23. 23.
    Butz, M., Kovacs, T., Lanzi, P.-L., Wilson, S.W.: How XCS Evolves Accurate Classifiers. In: Proceedings of the 2001 Genetic and Evolutionary Computation Conference – Gecco 2001, pp. 927–934. Morgan Kaufmann, San Francisco (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Larry Bull
    • 1
  1. 1.Faculty of Computing, Engineering & Mathematical SciencesUniversity of the West of EnglandBristolUK

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