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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Holland, J.H.: Adaptation. In: Rosen, R., Snell, F.M. (eds.) Progress in Theoretical Biology, vol. 4, pp. 263–293. Academic Press, London (1976)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
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)
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)
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)
Wilson, S.W.: ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1), 1–18 (1994)
Bull, L., Hurst, J.: ZCS Redux. Evolutionary Computation 10(2), 185–205 (2002)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
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)
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)
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)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)
Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–177 (1995)
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)
Dorigo, M.: Genetic and Non-Genetic Operators in ALECSYS. Evolutionary Computation 1(2), 151–164 (1993)
Smith, S.F.: A Learning System Based on Genetic Adaptive Algorithms. Ph.D. Dissertation, University of Pittsburgh (1980)
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)
Wilson, S.W.: Classifier Systems and the Animat Problem. Machine Learning 2, 199–228 (1987)
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)
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)
Deb, K.: Evolutionary Multiobjective Optimization Algorithms. Wiley, Chichester (2001)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bull, L. (2004). A Simple Payoff-Based Learning Classifier System. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_104
Download citation
DOI: https://doi.org/10.1007/978-3-540-30217-9_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
Online ISBN: 978-3-540-30217-9
eBook Packages: Springer Book Archive