Learning Classifier Systems

The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7-8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.

Balancing Specificity and Generality in a Panmictic-Based Rule-Discovery Learning Classifier System. - A Ruleset Reduction Algorithm for the XCS Learning Classifier System. - Adapted Pittsburgh-Style Classifier-System: Case-Study. - The Effect of Missing Data on Learning Classifier System Learning Rate and Classification Performance. - XCS s Strength-Based Twin: Part I. - XCS s Strength-Based Twin: Part II. - Further Comparison between ATNoSFERES and XCSM. - Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection. - Anticipatory Classifier System Using Behavioral Sequences in Non-Markov Environments. - Mapping Artificial Immune Systems into Learning Classifier Systems. - The 2003 Learning Classifier Systems Bibliography.