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IWLCS 2003 - 2005
- Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson:
Learning Classifier Systems, International Workshops, IWLCS 2003-2005, Revised Selected Papers. Lecture Notes in Computer Science 4399, Springer 2007, ISBN 978-3-540-71230-5
Knowledge Representation
- Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai:
Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS. 1-16 - Olgierd Unold, Grzegorz Dabrowski:
Use of Learning Classifier System for Inferring Natural Language Grammar. 17-24 - Toby O'Hara, Larry Bull:
Backpropagation in Accuracy-Based Neural Learning Classifier Systems. 25-39 - Xavier Llorà, Kumara Sastry, David E. Goldberg:
Binary Rule Encoding Schemes: A Study Using the Compact Classifier System. 40-58
Mechanisms
- Jaume Bacardit, Josep Maria Garrell i Guiu:
Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System. 59-79 - Flavio Baronti, Alessandro Passaro, Antonina Starita:
Post-processing Clustering to Decrease Variability in XCS Induced Rulesets. 80-92 - Yang Gao, Joshua Zhexue Huang, Hongqiang Rong, Daqian Gu:
LCSE: Learning Classifier System Ensemble for Incremental Medical Instances. 93-103 - Martin V. Butz
, David E. Goldberg, Pier Luca Lanzi:
Effect of Pure Error-Based Fitness in XCS. 104-114 - Ali Hamzeh
A Fuzzy System to Control Exploration Rate in XCS. 115-127 - Atsushi Wada, Keiki Takadama, Katsunori Shimohara:
Counter Example for Q-Bucket-Brigade Under Prediction Problem. 128-143 - Samuel Landau, Olivier Sigaud, Sébastien Picault, Pierre Gérard:
An Experimental Comparison Between ATNoSFERES and ACS. 144-160 - Albert Orriols-Puig, Ester Bernadó-Mansilla:
The Class Imbalance Problem in UCS Classifier System: A Preliminary Study. 161-180 - John H. Holmes, Jennifer A. Sager, Warren B. Bilker:
Three Methods for Covering Missing Input Data in XCS. 181-192
New Directions
- Javier G. Marín-Blázquez
A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients. 193-218 - Lashon B. Booker:
Adaptive Value Function Approximations in Classifier Systems. 219-238 - Stewart W. Wilson:
Three Architectures for Continuous Action. 239-257 - Lawrence Davis:
A Formal Relationship Between Ant Colony Optimizers and Classifier Systems. 258-269 - John H. Holmes:
Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis. 270-281
Application-Oriented Research and Tools
- Jaume Bacardit, Martin V. Butz
Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. 282-290 - Jaume Bacardit, David E. Goldberg, Martin V. Butz
Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule. 291-307 - David Ian Wyatt, Larry Bull, Ian C. Parmee:
Using XCS to Describe Continuous-Valued Problem Spaces. 308-332 - John H. Holmes, Jennifer A. Sager:
The EpiXCS Workbench: A Tool for Experimentation and Visualization. 333-344
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