The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005).

Knowledge Representation.- Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS.- Use of Learning Classifier System for Inferring Natural Language Grammar.- Backpropagation in Accuracy-Based Neural Learning Classifier Systems.- Binary Rule Encoding Schemes: A Study Using the Compact Classifier System.- Mechanisms.- Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System.- Post-processing Clustering to Decrease Variability in XCS Induced Rulesets.- LCSE: Learning Classifier System Ensemble for Incremental Medical Instances.- Effect of Pure Error-Based Fitness in XCS.- A Fuzzy System to Control Exploration Rate in XCS.- Counter Example for Q-Bucket-Brigade Under Prediction Problem.- An Experimental Comparison Between ATNoSFERES and ACS.- The Class Imbalance Problem in UCS Classifier System: A Preliminary Study.- Three Methods for Covering Missing Input Data in XCS.- New Directions.- A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients.- Adaptive Value Function Approximations in Classifier Systems.- Three Architectures for Continuous Action.- A Formal Relationship Between Ant Colony Optimizers and Classifier Systems.- Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis.- Application-Oriented Research and Tools.- Data Mining in Learning Classifier Systems: Comparing XCS with GAssist.- Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule.- Using XCS to Describe Continuous-Valued Problem Spaces.- The EpiXCS Workbench: A Tool for Experimentation and Visualization.

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