Abstract
Classifier systems have traditionally taken binary strings as inputs, yet in many real problems such as data inference, the inputs have real components. A modified XCS classifier system is described that learns a non-linear real-vector classification t
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
Bonarini, A.: Evolutionary learning of fuzzy rules: competition and cooperation. In: W. Pedrycz (ed.), Fuzzy Modelling: Paradigms and Practice. Norwell, MA: Kluwer Academic Press (1996) 265–284.
Holland, J. H.: Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: R. S. Michalski, J. G. Carbonell & T. M. Mitchell (Eds.), Machine Learning, An Artificial Intelligence Approach. Volume II. Los Altos, California: Morgan Kaufmann (1986).
Holmes, J. H.: Discovering risk of disease with a learning classifier system. In T. Baeck, ed., Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97). Morgan Kaufmann, San Francisco, CA. (1998) 426–433.
Kovacs, T.: XCS classifier system reliably evolves accurate, complete, and minimal representations for Boolean functions. In: Roy, Chawdhry and Pant (Eds), Soft Computing in Engineering Design and Manufacturing, Springer-Verlag London (1997) 59–68. (1999) 329–336.
Lanzi, P. L. and Colombetti, M.: An extension to the XCS classifier system for stochastic environments. In: Banzhaf, W., et al, (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA (1999) 353–360.
Lanzi, P. L. and Perrucci, A.: Extending the representation of classifier conditions, part II: from messy coding to s-expressions. In: Banzhaf, W., et al, (eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann: San Francisco, CA (1999) 345–352.
Rechenberg, I.: Evolutionsstrategie’ 94. Stuttgart-Bad Cannstatt: Frommann-Holzboog (1994).
Saxon, S. and Barry, A.: XCS and the Monk’s Problems. This volume.
Wilson, S. W.: ZCS: a zeroth level classifier system. Evolutionary Computation 2(1) (1994) 1–18.
Wilson, S. W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2) (1995) 149–175.
Wilson, S. W.: Generalization in the XCS classifier system. In Proceedings of the Third Annual Genetic Programming Conference, J. Koza et al (eds.). San Francisco, CA: Morgan Kaufmann (1998) 665–674.
Wilson, S. W.: State of XCS classifier system research. This volume.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wilson, S.W. (2000). Get Real! XCS with Continuous-Valued Inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 1999. Lecture Notes in Computer Science(), vol 1813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45027-0_11
Download citation
DOI: https://doi.org/10.1007/3-540-45027-0_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67729-1
Online ISBN: 978-3-540-45027-6
eBook Packages: Springer Book Archive