Advances at the frontier of LCS: First step completed
11 February 2006The 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.
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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.
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