Abstract
This paper presents an approach to analyze population evolution in classifier systems using a symbolic representation. Given a sequence of populations, representing the evolution of a solution, the method simplifies the classifiers in the populations by reducing them to their “canonical form”. Then, it extracts all the subexpressions that appear in all the classifier conditions and, for each subexpression, it computes the number of occurrences in each population. Finally, it computes the trend of all the subexpressions considered. The expressions which show an increasing trend through the course of evolution are viewed as building blocks that the system has used to construct the solution.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Booker, L.B.: Representing Attribute-Based Concepts in a Classifier System. In: Gregory, J.E. (ed.) Proceedings of the First Workshop on Foundations of Genetic Algorithms (FOGA 1991), pp. 115–127. Morgan Kaufmann, San Mateo (1991)
Dignum, S., Poli, R.: Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1588–1595. ACM Press, New York (2007)
Koza, J.R.: Hierarchical automatic function definition in genetic programming. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2, Vail, Colorado, USA, 24–29, 1992, pp. 297–318. Morgan Kaufmann, San Francisco (1992)
Lanzi, P.L.: Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding. In: Banzhaf, W., et al. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando (FL), July 1999, pp. 337–344. Morgan Kaufmann, San Francisco (1999)
Lanzi, P.L.: Mining interesting knowledge from data with the XCS classifier system. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 7-11 July 2001, pp. 958–965. Morgan Kaufmann, San Francisco (2001)
Lanzi, P.L., Perrucci, A.: Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions. In: Banzhaf, W., et al. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando (FL), July 1999, pp. 345–352. Morgan Kaufmann, San Francisco (1999)
Luke, S., Panait, L.: A comparison of bloat control methods for genetic programming. Evolutionary Computation 14, 309–344 (2006)
Poli, R.: A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 204–217. Springer, Heidelberg (2003)
Wolfram Research. Mathematica 5, http://www.wolfram.com
Rocca, S., Solari, S.: Building blocks analysis and exploitation in genetic programming. Master’s thesis (April 2006) Master thesis supervisor: Prof. Pier Luca Lanzi. Electronic version available from, http://www.dei.polimi.it/people/lanzi
Schaffer, J.D. (ed.): Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA 1989), George Mason University, June 1989. Morgan Kaufmann, San Francisco (1989)
Schuurmans, D., Schaeffer, J.: Representational Difficulties with Classifier Systems. In: Schaffer (ed.) [11], pp. 328–333, http://www.cs.ualberta.ca/~jonathan/Papers/Papers/classifier.ps
Sen, S.: A Tale of two representations. In: Proc. 7th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 245–254 (1994)
Shu, L., Schaeffer, J.: VCS: Variable Classifier System. In: Schaffer (ed.) [11], pp. 334–339, http://www.cs.ualberta.ca/~jonathan/Papers/Papers/vcs.ps
Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Booker, L., Forrest, S., Mitchell, M., Riolo, R.L. (eds.) Festschrift in Honor of John H. Holland, pp. 111–121. Center for the Study of Complex Systems (1999), http://prediction-dynamics.com/
Wilson, S.W.: Mining Oblique Data with XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996. Springer, Heidelberg (2001)
Wilson, S.W.: Mining oblique data with XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 158–176. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lanzi, P.L., Rocca, S., Sastry, K., Solari, S. (2008). Analysis of Population Evolution in Classifier Systems Using Symbolic Representations. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-88138-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88137-7
Online ISBN: 978-3-540-88138-4
eBook Packages: Computer ScienceComputer Science (R0)