Effect of Pure Error-Based Fitness in XCS

  • Martin V. Butz
  • David E. Goldberg
  • Pier Luca Lanzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4399)

Abstract

The accuracy-based fitness approach in XCS is one of the most significant changes in comparison with original learning classifier systems. Nonetheless, neither the scaled accuracy function, nor the importance of the relative fitness approach has been investigated in detail. The recent introduction of tournament selection to XCS has shown to make the system more independent from parameter settings and scaling issues. The question remains if relative accuracy itself is actually necessary in XCS or if the evolutionary process could be based directly on error. This study investigates advantages and disadvantages of pure error-based fitness vs. relative accuracy-based fitness in XCS.

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References

  1. 1.
    Barry, A.: A hierarchical XCS for long path environments. In: Proceedings of the Third Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 913–920 (2001)Google Scholar
  2. 2.
    Bernadó, E., Llorà, X., Garrell, J.M.: XCS and GALE: A comparative study of two learning classifier systems and six other learning algorithms on classification tasks. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 115–132. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Bull, L., Hurst, J.: ZCS redux. Evolutionary Computation 10(2), 185–205 (2002)CrossRefGoogle Scholar
  4. 4.
    Butz, M.V.: Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Butz, M.V., et al.: Bounding the population size to ensure niche support in XCS. IlliGAL report 2004033, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2004)Google Scholar
  6. 6.
    Butz, M.V., Goldberg, D.E., Tharakunnel, K.: Analysis and improvement of fitness exploitation in XCS: Bounding models, tournament selection, and bilateral accuracy. Evolutionary Computation 11, 239–277 (2003)CrossRefGoogle Scholar
  7. 7.
    Butz, M.V., et al.: How XCS evolves accurate classifiers. In: Proceedings of the Third Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 927–934 (2001)Google Scholar
  8. 8.
    Butz, M.V., et al.: Extracted global structure makes local building block processing effective in XCS. In: GECCO 2005: Genetic and Evolutionary Computation Conference, vol. 1, pp. 655–662 (2005)Google Scholar
  9. 9.
    Butz, M.V., Sastry, K., Goldberg, D.E.: Tournament selection in XCS. In: Proceedings of the Fifth Genetic and Evolutionary Computation Conference (GECCO-2003), pp. 1857–1869 (2003)Google Scholar
  10. 10.
    Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 253–272. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Dixon, P.W., Corne, D.W., Oates, M.J.: A preliminary investigation of modified XCS as a generic data mining tool. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 133–150. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Kovacs, T.: Deletion schemes for classifier systems. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), pp. 329–336 (1999)Google Scholar
  13. 13.
    Kovacs, T.: Strength or Accuracy? Fitness calculation in learning classifier systems. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) Learning Classifier Systems. LNCS (LNAI), vol. 1813, pp. 143–160. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Kovacs, T.: Towards a theory of strong overgeneral classifiers. In: Foundations of Genetic Algorithms 6, pp. 165–184 (2001)Google Scholar
  15. 15.
    Lanzi, P.L.: An analysis of generalization in the XCS classifier system. Evolutionary Computation 7(2), 125–149 (1999)CrossRefGoogle Scholar
  16. 16.
    Lanzi, P.L.: An extension to the XCS classifier system for stochastic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), pp. 353–360 (1999)Google Scholar
  17. 17.
    Lanzi, P.L., et al.: Extending XCSF beyond linear approximation. In: GECCO 2005: Genetic and Evolutionary Computation Conference: vol. 2, pp. 1827–1834 (2005)Google Scholar
  18. 18.
    Lanzi, P.L., et al.: Generalization in XCSF for real inputs. IlliGAL report 2005023, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2005)Google Scholar
  19. 19.
    Venturini, G.: Adaptation in dynamic environments through a minimal probability of exploration. In: From Animals to Animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pp. 371–381 (1994)Google Scholar
  20. 20.
    Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, Englewood Cliffs (1985)MATHGoogle Scholar
  21. 21.
    Wilson, S.W.: ZCS: A zeroth level classifier system. Evolutionary Computation 2, 1–18 (1994)CrossRefGoogle Scholar
  22. 22.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar
  23. 23.
    Wilson, S.W.: Generalization in the XCS classifier system. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 665–674 (1998)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Martin V. Butz
    • 1
  • David E. Goldberg
    • 2
  • Pier Luca Lanzi
    • 2
    • 3
  1. 1.Department of Cognitive Psychology, University of Würzburg, 97070 WürzburgGermany
  2. 2.Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IllinoisUSA
  3. 3.Dipartimento di Elettronica e Informazione, Politecnico di Milano, MilanoItaly

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