@inproceedings{wilson:1998:gXCScs,
	title = {Generalization in the {XCS} Classifier System},
	address = {University of Wisconsin, Madison, Wisconsin, USA},
	author = {Stewart W. Wilson},
	booktitle = {Genetic Programming 1998: Proceedings of the Third
                 Annual Conference},
	editor = {John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo},
	month = {22-25 July},
	pages = {665--674},
	publisher = {Morgan Kaufmann},
	url = {http://citeseer.ist.psu.edu/148764.html},
	year = {1998},
	biburl = {http://www.bibsonomy.org/bibtex/2a78f8027c99653c080eac7050aeb3cf1/brazovayeye},
	abstract = {This paper studies two changes to XCS, a classifier
                 system in which fitness is based on prediction accuracy
                 and the genetic algorithm takes place in environmental
                 niches. The changes were aimed at increasing XCS's
                 tendency to evolve accurate, maximally general
                 classifiers and were tested on previously employed
                 {"}woods{"} and multiplexer tasks. Together the changes
                 bring XCS close to evolving populations whose
                 high-fitness classifiers form a near-minimal, accurate,
                 maximally general cover of the input and action product
                 space. In addition, results on the multiplexer, a
                 difficult categorization task, suggest that XCS's
                 learning complexity is polynomial in the input length
                 and thus may avoid the {"}curse of dimensionality{"}, a
                 notorious barrier to scale-up. A comparison between XCS
                 and genetic programming in solving the 6multiplexer
                 suggests that XCS's learning rate is about three orders
                 of magnitude faster in terms of the number of input
                 instances processed.},
	publisher_address = {San Francisco, CA, USA}, isbn = {1-55860-548-7}, notes = {GP-98},
	keywords = {algorithms, classifiers genetic }
}