LCS and other GBML

Archive for 2006

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List of papers to be presented at IWLCS 2006

4 May 2006

This is the list of papers accepted for presentation at IWLCS 2006 that will take place during GECCO 2006.

• Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System
  Bacardit, J. and Krasnogor, N.
• An Artificial Life Classifier System for Real-Valued Inputs
  Bishop, J.
• Technology Extraction for Future Generations from Process Time Series Data Reflecting Expert Operator Skills
  Kurahashi, S. and Terano, T.
• An Initial Analysis of Parameter Sensitivity for XCS with Computed Prediction
  Lanzi, P.L., and Zanini, M.
• The χ-ary Extended Compact Classifier System: Linkage Learning in Pittsburgh LCS
  Llorà, X., Sastry, K., Goldberg, D.E., and delaOssa, L.
• Using XCS for Action Selection in RoboCup Rescue Simulation League
  Martínez, I. C., Ojeda, D., and Zamora, E.
• Extending XCS with Representation in First-Order Logic
  Mellor, D.
• A Further Look at UCS Classifier System
  Orriols-Puig, A., Bernad&oaccute;-Mansilla, E.
• Agent-Based Learning Classifier Systems for Grid Data Mining
  Santos, M.F, Quintela, H., and Neves, J.
• Community of Practice under Learning Classifier Systems
  Suematsu, Y.I.L., Takadama, K., Shimohara, K., and Katai, O.
• Developing Conversational Interfaces with XCS
  Toney, D., Moore, J., and Lemon, O.
• Dual-structured Classifier System Mediating XCS and Gradient Descent based Update
  Wada, A., Takadama, K., and Shimohara, K.

IWLCS 2006 papers under review

10 April 2006

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.

LCSWeb creates a LCS and GBML paper database

20 February 2006

Jan Drugowitsch in agreement with Tim Kovacs have team up to provide a LCS and other GBML paper database. You can access it here. You can also search the IlliGAL biblography at the LCS and other GBML web site (rigth-hand side search boxes).

LCS software

20 February 2006

If you are looking for some freely available LCS software, you can find a list maintained by Jan Drugowitsch here.

Advances at the frontier of LCS: First step completed

11 February 2006

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.

Multi-Objective Machine Learning

31 January 2006

The book Multi-objective Machine Learning edited by Yaochu Jin contains several chapters on the usage of LCS and GBML for multi-objective machine learning. Among other topics it includes the usage of multi-objective optimization to evolve accurate and compact rule sets using LCS and GBML, and the use of GA-based Pareto optimization for rule extraction from neural networks.

Rule-Based Evolutionary Online Learning Systems

11 January 2006

This book by Martin Butz offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.

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