Learning Classifier Systems: International Workshops, IWLCS 2003-2005, Revised Selected Papers (Lecture Notes in Computer Science)

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. 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 - The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions. The topics presented in this volume summarize the wide spectrum of interests of the Learning Classi?er Systems (LCS) community. The topics range from theoretical analysis of mechanisms to practical cons- eration for successful application of such techniques to everyday data-mining tasks. When we started editing this volume, we faced the choice of organizing the contents in a purely chronologicalfashion or as a sequence of related topics that help walk the reader across the di?erent areas. In the end we decided to or- nize the contents by area, breaking the time-line a little. This is not a simple endeavor as we can organize the material using multiple criteria. The tax- omy below is our humble e?ort to provide a coherent grouping. Needless to say, some works may fall in more than one category. The four areas are as follows: Knowledge representation. These chapters elaborate on the knowledge r- resentations used in LCS. Knowledge representation is a key issue in any learning system and has implications for what it is possible to learn and what mechanisms shouldbe used. Four chapters analyze di?erent knowledge representations and the LCS methods used to manipulate them.

This book constitutes the thoroughly refereed joint post-proceedings of 3 consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL, USA in July 2003, in Seattle, WA, USA in June 2004, and in Washington, DC, USA 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 everday datamining tasks.

This book constitutes the thoroughly refereed joint post-proceedings of 3 consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL, USA in July 2003, in Seattle, WA, USA in June 2004, and in Washington, DC, USA 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 everday datamining tasks 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. Topics In The 22 Revised Full Papers Range From Theoretical Analysis Of Mechanisms To Practical Consideration For Successful Application Of Such Techniques To Everyday Datamining Tasks.