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International Workshop on Learning Classifier Systems

International Workshop on Learning Classifier Systems

IWLCS 2009, IWLCS 2008: Learning Classifier Systems pp 57–69Cite as

Current XCSF Capabilities and Challenges

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6471))

Abstract

Function approximation is an important technique used in many different domains, including numerical mathematics, engineering, and neuroscience. The XCSF classifier system is able to approximate complex multi-dimensional function surfaces using a patchwork of simpler functions. Typically, locally linear functions are used due to the tradeoff between expressiveness and interpretability. This work discusses XCSF’s current capabilities, but also points out current challenges that can hinder learning success. A theoretical discussion on when XCSF works is intended to improve the comprehensibility of the system. Current advances with respect to scalability theory show that the system constitutes a very effective machine learning technique. Furthermore, the paper points-out how to tune relevant XCSF parameters in actual applications and how to choose appropriate condition and prediction structures. Finally, a brief comparison to the Locally Weighted Projection Regression (LWPR) algorithm highlights positive as well as negative aspects of both methods.

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References

  1. Holland, J.H.: Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. The MIT Press, Cambridge (1992)

    Google Scholar 

  2. Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)

    Article  Google Scholar 

  3. Butz, M.V., Goldberg, D.E., Lanzi, P.L.: Gradient descent methods in learning classifier systems: Improving XCS performance in multistep problems. Technical report, Illinois Genetic Algorithms Laboratory (2003)

    Google Scholar 

  4. Bernadó-Mansilla, E., Garrell-Guiu, J.M.: Accuracy-based learning classifier systems: Models, analysis, and applications to classification tasks. Evolutionary Computation 11, 209–238 (2003)

    Article  Google Scholar 

  5. Butz, M.V.: Rule-Based Evolutionary Online Learning Systems: A Principal Approach to LCS Analysis and Design. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  6. Butz, M.V., Herbort, O.: Context-dependent predictions and cognitive arm control with XCSF. In: GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1357–1364. ACM, New York (2008)

    Google Scholar 

  7. Stalph, P.O., Butz, M.V., Pedersen, G.K.M.: Controlling a four degree of freedom arm in 3D using the XCSF learning classifier system. In: Mertsching, B., Hund, M., Aziz, Z. (eds.) KI 2009. LNCS, vol. 5803, pp. 193–200. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  8. Wilson, S.W.: Classifiers that approximate functions. Natural Computing 1, 211–234 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  9. Stalph, P.O., Llorà, X., Goldberg, D.E., Butz, M.V.: Resource Management and Scalability of the XCSF Learning Classifier System. Theoretical Computer Science (in press), http://dx.doi.org/10.1016/j.tcs.2010.07.007

  10. Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.W.: How XCS evolves accurate classifiers. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 927–934 (2001)

    Google Scholar 

  11. Wright, A.H.: Genetic algorithms for real parameter optimization. In: Foundations of Genetic Algorithms, pp. 205–218. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  12. Goldberg, D.E.: Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems 5, 139–167 (1991)

    MathSciNet  MATH  Google Scholar 

  13. Radcliffe, N.J.: Equivalence class analysis of genetic algorithms. Complex Systems 5, 183–205 (1991)

    MathSciNet  MATH  Google Scholar 

  14. Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm – I. continuous parameter optimization. Evolutionary Computation 1, 25–49 (1993)

    Google Scholar 

  15. Beyer, H.G., Schwefel, H.P.: Evolution strategies - a comprehensive introduction. Natural Computing 1(1), 3–52 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  16. Bosman, P.A.N., Thierens, D.: Numerical optimization with real-valued estimation-of-distribution algorithms. In: Scalable Optimization via Probabilistic Modeling. SCI, vol. 33, pp. 91–120. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Stalph, P.O., Butz, M.V.: How Fitness Estimates Interact with Reproduction Rates: Towards Variable Offspring Set Sizes in XCSF. In: Bacardit, J. (ed.) IWLCS 2008/2009. LNCS (LNAI), vol. 6471, pp. 47–56. Springer, Heidelberg (2010)

    Google Scholar 

  18. Orriols-Puig, A., Bernadó-Mansilla, E.: Bounding XCS’s parameters for unbalanced datasets. In: GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1561–1568. ACM, New York (2006)

    Google Scholar 

  19. Kovacs, T., Kerber, M.: What makes a problem hard for XCS? In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 251–258. Springer, Heidelberg (2001)

    Google Scholar 

  20. Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 209–219. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  21. 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 

  22. Stone, C., Bull, L.: For real! XCS with continuous-valued inputs. Evolutionary Computation 11(3), 299–336 (2003)

    Article  Google Scholar 

  23. Butz, M.V., Lanzi, P.L., Wilson, S.W.: Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Transactions on Evolutionary Computation 12, 355–376 (2008)

    Article  Google Scholar 

  24. Wilson, S.W.: Classifier conditions using gene expression programming. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds.) IWLCS 2006 and IWLCS 2007. LNCS (LNAI), vol. 4998, pp. 206–217. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  25. Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Extending XCSF beyond linear approximation. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 1827–1834 (2005)

    Google Scholar 

  26. Vijayakumar, S., Schaal, S.: Locally weighted projection regression: An O(n) algorithm for incremental real time learning in high dimensional space. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 1079–1086 (2000)

    Google Scholar 

  27. Vijayakumar, S., D’Souza, A., Schaal, S.: Incremental online learning in high dimensions. Neural Computation 17(12), 2602–2634 (2005)

    Article  MathSciNet  Google Scholar 

  28. Stalph, P.O., Rubinsztajn, J., Sigaud, O., Butz, M.V.: A comparative study: Function approximation with LWPR and XCSF. In: GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (in press, 2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Cognitive Psychology III, University of Würzburg, Röntgenring 11, 97080, Würzburg, Germany

    Patrick O. Stalph & Martin V. Butz

Authors
  1. Patrick O. Stalph
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  2. Martin V. Butz
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Editor information

Editors and Affiliations

  1. School of Computer Science, ASAP research group, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, LE12 5RD, Sutton Bonington, UK

    Jaume Bacardit

  2. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, 6140, Wellington, New Zealand

    Will Browne

  3. Department of Brain and Cognitive Sciences, University of Rochester, Meliora Hall, 14627, Rochester, NY, USA

    Jan Drugowitsch

  4. Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins, 2, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  5. Department of Psychology III, University of Würzburg, COBOSLAB – Cognitive Bodyspaces: Learning and Behavior,, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

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Stalph, P.O., Butz, M.V. (2010). Current XCSF Capabilities and Challenges. In: Bacardit, J., Browne, W., Drugowitsch, J., Bernadó-Mansilla, E., Butz, M.V. (eds) Learning Classifier Systems. IWLCS IWLCS 2009 2008. Lecture Notes in Computer Science(), vol 6471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17508-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-17508-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17507-7

  • Online ISBN: 978-3-642-17508-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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