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.