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Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann and Stewart W. Wilson
Front matter
1-16
Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS
17-24
Use of Learning Classifier System for Inferring Natural Language Grammar
25-39
Backpropagation in Accuracy-Based Neural Learning Classifier Systems
40-58
Binary Rule Encoding Schemes: A Study Using the Compact Classifier System
59-79
Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System
80-92
Post-processing Clustering to Decrease Variability in XCS Induced Rulesets
93-103
LCSE: Learning Classifier System Ensemble for Incremental Medical Instances
104-114
Effect of Pure Error-Based Fitness in XCS
115-127
A Fuzzy System to Control Exploration Rate in XCS
128-143
Counter Example for Q-Bucket-Brigade Under Prediction Problem
144-160
An Experimental Comparison Between ATNoSFERES and ACS
161-180
The Class Imbalance Problem in UCS Classifier System: A Preliminary Study
181-192
Three Methods for Covering Missing Input Data in XCS
193-218
A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients
219-238
Adaptive Value Function Approximations in Classifier Systems
239-257
Three Architectures for Continuous Action
258-269
A Formal Relationship Between Ant Colony Optimizers and Classifier Systems
270-281
Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis
282-290
Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
291-307
Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
308-332
Using XCS to Describe Continuous-Valued Problem Spaces
333-344
The EpiXCS Workbench: A Tool for Experimentation and Visualization
Back matter
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