Abstract
XCSF is a new extension to XCS that is developed to extend XCS’s reward calculation capability via computing. This new feature is called computable prediction. The first version of XCSF tries to find the most appropriate equation to compute each classifier’s reward using a weight update mechanism. In this paper, we try to propose a new evolutionary mechanism to compute these equations using genetic algorithms.
Chapter PDF
Similar content being viewed by others
References
Wilson, S.W.: Classifiers that approximate functions. Journal of Natural Computating 1(2-3), 211–234 (2002)
Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3 (1995)
Widrow, B., Hoff, M.E.: Adaptive Switching Circuits, Chapter Neurocomputing: Foundation of Research, pp. 126–134. The MIT Press, Cambridge (1988)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension. Technical Report 2005012, Illinois Genetic Algorithms Laboratory (2005)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975); Republished by the MIT press (1992)
Wilson, S.W.: Function approximation with a classifier system. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 974–981. Morgan Kaufmann, San Francisco (2001)
Wilson, S.W.: Classifier systems for continuous payoff environments. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 824–835. Springer, Heidelberg (2004)
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. Journal of Soft Computing 6(3-4), 144–153 (2002)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Englewood Cliffs (1998)
Butz, M.V., Sastry, K., Goldberg, D.E.: Tournament selection: Stable fitness pressure in XCS. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1857–1869. Springer, Heidelberg (2003)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: XCS with Computable Prediction in Multistep Environments. Technical Report 2005008, Illinois Genetic Algorithms Laboratory (2005)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Extending XCSF Beyond Linear Approximation. Illinois Genetic Algorithms Laboratory University of Illinois at Urbana-Champaign (2005)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: XCS with Computable Prediction for the Learning of Boolean Functions. Technical Report 2005007, Illinois Genetic Algorithms Laboratory (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hamzeh, A., Rahmani, A. (2005). An Evolutionary Function Approximation Approach to Compute Prediction in XCSF. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science(), vol 3720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564096_57
Download citation
DOI: https://doi.org/10.1007/11564096_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29243-2
Online ISBN: 978-3-540-31692-3
eBook Packages: Computer ScienceComputer Science (R0)