“Advances at the frontier of Learning Classifier Systems” has been shipped to Springer for the final stages of editing and printing. The volume is going to be printed as Springer’s LNCS 4399 volume. When we started editing this volume, we faced the choice of organizing the contents in a purely chronological fashion or as a sequence of related topics that help walk the reader across the different areas. In the end we decided to organize the contents by area, breaking a little the time-line. This was not a simple endeavor as we could organize the material using multiple criteria. The taxonomy below is our humble effort to provide a coherent grouping. Needless to say, some works may fall in more than one category. Below, you may find the tentative table of contents of the volume. It may change a little bit, but we will keep you posted as soon as we learn from Springer.
Part I. Knowledge representation
- 1. Analyzing Parameter Sensitivity and Classifier Representations for Real-valued XCS
by Atsushi Wada, Keiki Takadama, Katsunori Shimohara, and Osamu Katai
4399 – 001 - 2. Use of Learning Classifier System for Inferring Natural Language Grammar
by Olgierd Unold and Grzegorz Dabrowski
4399 – 018 - 3. Backpropagation in Accuracy-based Neural Learning Classifier Systems
by Toby O’Hara and Larry Bull
4399 – 026 - 4. Binary Rule Encoding Schemes: A Study Using The Compact Classifier System
by Xavier Llorà , Kumara Sastry , and David E. Goldberg
4399 – 041
Part II. Mechanisms
- 5. Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System
by Jaume Bacardit and Josep Maria Garrell
4399 – 061 - 6. Post-processing Clustering to Decrease Variability in XCS Induced Rulesets
by Flavio Baronti, Alessandro Passaro, and Antonina Starita
4399 – 081 - 7. LCSE: Learning Classifier System Ensemble for Incremental Medical Instances
by Yang Gao, Joshua Zhexue Huang, Hongqiang Rong, and Da-qian Gu
4399 – 094 - 8. Effect of Pure Error-Based Fitness in XCS
by Martin V. Butz , David E. Goldberg, and Pier Luca Lanzi
4399 – 105 - 9. A Fuzzy System to Control Exploration Rate in XCS
by Ali Hamzeh and Adel Rahmani
4399 – 116 - 10. Counter Example for Q-bucket-brigade under Prediction Problema
by Atsushi Wada, Keiki Takadama, and Katsunori Shimohara
4399 – 130 - 11. An Experimental Comparison between ATNoSFERES and ACS
by Samuel Landau, Olivier Sigaud, Sébastien Picault, and Pierre Gérard
4399 – 146 - 12. The Class Imbalance Problem in UCS Classifier System: A Preliminary Study
by Albert Orriols-Puig and Ester Bernadó-Mansilla
4399 – 164 - 13. Three Methods for Covering Missing Input Data in XCS
by John H. Holmes, Jennifer A. Sager, and Warren B. Bilker
4399 – 184
Part III. New Directions
- 14. A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients
by Javier G. MarÃn-Blázquez and Sonia Schulenburg
4399 – 197 - 15. Adaptive value function approximations in classifier systems
by Lashon B. Booker
4399 – 224 - 16. Three Architectures for Continuous Action
by Stewart W. Wilson
4399 – 244 - 17. A Formal Relationship Between Ant Colony Optimizers and Classifier Systems
by Lawrence Davis
4399 – 263 - 18. Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis
by John H. Holmes
4399 – 276
Part IV. Application-oriented research and tools
- 19. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
by Jaume Bacardit and Martin V. Butz
4399 – 290 - 20. Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
by Jaume Bacardit, David E. Goldberg, and Martin V. Butz
4399 – 299 - 21. Using XCS to Describe Continuous-Valued Problem Spaces
by David Wyatt, Larry Bull, and Ian Parmee
4399 – 318 - 22. The EpiXCS Workbench: A Tool for Experimentation and Visualization
by John H. Holmes and Jennifer A. Sager
4399 – 343