Advances at the frontier of LCS: LNCS 4399

“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. Marín-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
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