This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005--all hosted by the Genetic and Evolutionary Computation Conference, GECCO.
The 22 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, mechanisms, new directions, as well as application-oriented research and tools. The topics range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.
Author(s): Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai (auth.), Tim Kovacs, Xavier Llorà , Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson (eds.)
Series: Lecture Notes in Computer Science 4399 : Lecture Notes in Artificial Intelligence
Edition: 1
Publisher: Springer Berlin Heidelberg
Year: 2007
Language: English
Pages: XII, 345 p. Also available online.
Content:
Front Matter....Pages -
Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS....Pages 1-16
Use of Learning Classifier System for Inferring Natural Language Grammar....Pages 17-24
Backpropagation in Accuracy-Based Neural Learning Classifier Systems....Pages 25-39
Binary Rule Encoding Schemes: A Study Using the Compact Classifier System....Pages 40-58
Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System....Pages 59-79
Post-processing Clustering to Decrease Variability in XCS Induced Rulesets....Pages 80-92
LCSE: Learning Classifier System Ensemble for Incremental Medical Instances....Pages 93-103
Effect of Pure Error-Based Fitness in XCS....Pages 104-114
A Fuzzy System to Control Exploration Rate in XCS....Pages 115-127
Counter Example for Q-Bucket-Brigade Under Prediction Problem....Pages 128-143
An Experimental Comparison Between ATNoSFERES and ACS....Pages 144-160
The Class Imbalance Problem in UCS Classifier System: A Preliminary Study....Pages 161-180
Three Methods for Covering Missing Input Data in XCS....Pages 181-192
A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients....Pages 193-218
Adaptive Value Function Approximations in Classifier Systems....Pages 219-238
Three Architectures for Continuous Action....Pages 239-257
A Formal Relationship Between Ant Colony Optimizers and Classifier Systems....Pages 258-269
Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis....Pages 270-281
Data Mining in Learning Classifier Systems: Comparing XCS with GAssist....Pages 282-290
Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule....Pages 291-307
Using XCS to Describe Continuous-Valued Problem Spaces....Pages 308-332
The EpiXCS Workbench: A Tool for Experimentation and Visualization....Pages 333-344
Back Matter....Pages -
