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Initial Modifications to XCS for Use in Interactive Evolutionary Design

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Parallel Problem Solving from Nature — PPSN VII (PPSN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2439))

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Abstract

Learning classifier systems represent a technique by which various characteristics of a given problem space may be deduced and presented to the user in a readable format. In this paper we present results from the use of XCS on simple tasks with the general multi-variable features typically found in problems addressed by an Interactive Evolutionary Design process. That is, we examine the behaviour of XCS with versions of a well-known single-step task and consider the speed of learning and the ability to respond to changes. We introduce a simple form of supervised learning for XCS with the aim of improving its performance with respect to these two measures. Results show that improvements can be made under the new learning scheme and that other aspects of XCS can also play a significant role.

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References

  1. Baeck, T. (1998) On the Behaviour of Evolutionary Algorithms in Dynamic Environments. In Proceedings of the Fifth IEEE Conference on Evolutionary Computation, pages 446–451. IEEE Press, Piscataway (NJ).

    Google Scholar 

  2. Bull, L. & Hurst, J. (2001) ZCS: Theory and Practice. UWE Learning Classifier Systems Technical Report UWELCSG01-001.

    Google Scholar 

  3. Bull, L., Hurst, J. & Tomlinson, A. (2000) Self-Adaptive Mutation in Classifier System Controllers. In J-A. Meyer, A. Berthoz, D. Floreano, H. Roitblatt & S. W. Wilson (eds) From Animals to Animats 6—The Sixth International Conference on the Simulation of Adaptive Behaviour, MIT Press, Cambridge (MA).

    Google Scholar 

  4. Butz, M., Kovacs, T., Lanzi, P & Wilson, S. (2001) How XCS evolves accurate classifiers. In Proceedings of the Genetic and Evolutionary Computation Conference 2001, pages 927–934. Morgan Kaufmann, San Francisco (CA).

    Google Scholar 

  5. Butz, M. & Pelikan, M. (2001) Analyzing the Evolutionary Pressures in XCS. In Proceedings of the Genetic and Evolutionary Computation Conference 2001, pages 927–934. Morgan Kaufmann, San Francisco (CA).

    Google Scholar 

  6. Butz, M. V. and Wilson, S. W. (2001) An algorithmic description of XCS. In Lanzi, P. L., Stolzmann, W., and S. W. Wilson (Eds.), Advances in Learning Classifier Systems. Third International Workshop (IWLCS-2000), Lecture Notes in Artificial Intelligence (LNAI-1996). Berlin: Springer-Verlag (2001).

    Google Scholar 

  7. Dixon, P. W., Corne, D. W. & Oates, M. J.(2001) A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool. In Proceedings of the 2001 Genetic and Evolutionary Computation Conference Workshop Program, pp345–350.

    Google Scholar 

  8. Fu, C., Wilson S. & Davis, L. (2001) Studies of the XCSI Classifier System on a Data Mining Problem. In Proceedings of the Genetic and Evolutionary Computation Conference 2001, page 985. Morgan Kaufmann, San Francisco (CA).

    Google Scholar 

  9. Hartley, A (1999) Accuracy-based fitness allows similar performance to humans in static and dynamic classification environments. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, editors, GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, pages 266–273. Morgan Kaufmann, San Francisco (CA).

    Google Scholar 

  10. Holland, J. H. (1986). Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine learning, an artificial intelligence approach. Los Altos, California: Morgan Kaufmann.

    Google Scholar 

  11. Lanzi, P. (2001) Mining Interesting Knowledge from Data with the XCS Classifier System. In Proceedings of the Genetic and Evolutionary Computation Conference 2001, pages 958–965. Morgan Kaufmann, San Francisco (CA).

    Google Scholar 

  12. Parmee I. C., Cvetkovic C., Watson A. H., Bonham C. R. (2000): Multi-objective Satisfaction within an Interactive Evolutionary Design Environment. Evolutionary Computation. 8 (2), pp 197–222.

    Article  Google Scholar 

  13. Parmee I. C., Cvetkovic C., A. H., Bonham C. R., Packham I. (2001): Introducing Prototype Interactive Evolutionary Systems for Ill-defined Design Environments. Journal of Advances in Engineering Software, 32 (6), Elsevier, pp 429–441.

    Article  MATH  Google Scholar 

  14. Parmee I. C., Bonham C. R. (1999) Towards the Support of Innovative Conceptual Design Through Interactive Designer / Evolutionary Computing Strategies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing Journal; Cambridge University Press, 14, pp 3–16.

    Google Scholar 

  15. Parmee I. C. (2001a), Poor Definition, Uncertainty and Human Factors—Satisfying Multiple Objectives in Real-world Decision-making Environments. First International Conference on Evolutionary Multi-criterion Optimisation (EMO 2001); Lecture Notes in Computer Science No 1993, Springer; pp 52–66.

    Google Scholar 

  16. Parmee I. C., (2001b), Evolutionary and Adaptive Computing in Engineering Design. Springer-Verlag, London.

    Google Scholar 

  17. Saxon, S. & Barry, A. (2000) XCS and the Monk’s Problem. Lanzi, P. L., Stolzmann, W., and Wilson, S. W., eds. Learning Classifier Systems. From Foundations to Applications Lecture Notes in Artificial Intelligence (LNAI-1813) Berlin: Springer-Verlag.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  19. Wilson, S. W. (1998) Generalization in the XCS classifier system. In Genetic Programming 1998: Proceedings of the Third Annual Conference (pp. 665–674), J. Koza et al., eds., San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  20. Wilson, S. W. (2000) Get real! XCS with Continuous-valued inputs. Lanzi, P. L., Stolzmann, W., and Wilson, S. W., eds. Learning Classifier Systems. From Foundations to Applications Lecture Notes in Artificial Intelligence (LNAI-1813) Berlin: Springer-Verlag.

    Google Scholar 

  21. Wilson, S. W. (2001) Mining Oblique Data with XCS. In Lanzi, P. L., Stolzmann, W., and Wilson, S. W., eds. Advances in Learning Classifier Systems. Third International Workshop(IWLCS-2000), (LNAI-1996) Berlin: Springer-Verlag.

    Google Scholar 

  22. Hurst, J. & Bull, L. (2000) Self-Adaptation in Learning Classifier Systems. UWE Learning Classifier Systems Technical Report UWELCSG00-001.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computing, Engineering & Mathematical Sciences, University of the West of England, Bristol

    Larry Bull, David Wyatt & Ian Parmee

Authors
  1. Larry Bull
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  2. David Wyatt
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  3. Ian Parmee
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Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Informática, 18071, Granada, Spain

    Juan Julián Merelo Guervós

  2. Department of Informatics, Technological Educational Institute of Thessaloniki, 54101, Thessaloniki, Greece

    Panagiotis Adamidis

  3. Department of Informatics XI, University of Dortmund, 44221, Dortmund, Germany

    Hans-Georg Beyer  & Hans-Paul Schwefel  & 

  4. Department of Signal Theory and Communications, Universidad Carlos III, Madrid, Spain

    José-Luis Fernández-Villacañas

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© 2002 Springer-Verlag Berlin Heidelberg

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Bull, L., Wyatt, D., Parmee, I. (2002). Initial Modifications to XCS for Use in Interactive Evolutionary Design. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_55

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  • DOI: https://doi.org/10.1007/3-540-45712-7_55

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44139-7

  • Online ISBN: 978-3-540-45712-1

  • eBook Packages: Springer Book Archive

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