LCS & GBML Central

The 12 papers submitted to IWLCS 2006 are in the process of being reviewed by the IWLCS 2006 program committee. The decisions will be emailed to the authors shortly.

The first step of the volume Advances at the frontier of LCS is almost done. Below there is a list of the camera readies collected so far. These book chapters cover the contributions to IWLCS on 2003 and 2004.

• Data Mining in Learning Classifier Systems: Comparing XCS with GAssist.
  Bacardit, J. and Butz, M.
• Bloat Control and Generalization Pressure using the Minimum Description Length Principle for a Pittsburgh approach Learning Classifier System.
  Bacardit, J. and Garrell, J.M.
• Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule.
  Bacardit, J., Goldberg, D.E., and Butz, M.
• Effect of Pure Error-Based Fitness in XCS.
  Butz, M., Goldberg, D.E., and Lanzi, P.L.
• A Formal Relationship Between Ant Colony Optimizers and Classifier Systems.
  Davis, D.
• An Experimental Comparison between ATNoSFERES and ACS.
  Landau, S., Sigaud, O., Picault, S., and Gérard, P.
• Where to Go Once You Have Evolved a Bunch of Promising Hypotheses?.
  Llorà, X., Bernadó, B., Bacardit, J., and Traus, I.
• A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients.
  Martín-Blazquez, J. and Schulenburg, S.
• Backpropagation in Accuracy-based Neural Learning Classifier Systems .
  O’Hara, T. and Bull, L.
• Use of Learning Classifier System for Inferring Natural Language Grammar .
  Unold, O. and Dabrowski, G.
• Analyzing Parameter Sensitivity and Classifier Representations for Real-valued XCS .
  Wada, A., Takadama, K., Shimohara, K., and Katai, O.
• Three Architectures for Continuos Action.
  Wilson, S.W.
• Using XCS to Describe Continuous-Valued Problem Spaces.
  Wyatt, D., Bull, L., and Parmee, I.