The book Design and Analysis of Learning Classifier Systems: A Probabilistic Approach by Jan Drugowitsch presents a machine learning approach to Learning Classifier Systems. In the author’s own words:

This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems. Contrary to commonly approaching their design and analysis from the viewpoint of evolutionary computation, this book instead promotes a probabilistic model-based approach, based on their defining question “What is an LCS supposed to learn?”. Systematically following this approach, it is shown how generic machine learning methods can be applied to design LCS algorithms from the first principles of their underlying probabilistic model, which is in this book  for illustrative purposes  closely related to the currently prominent XCS classifier system. The approach is holistic in the sense that the uniform goal-driven design metaphor essentially covers all aspects of LCS and puts them on a solid foundation, in addition to enabling the transfer of the theoretical foundation of the various applied machine learning methods onto LCS. Thus, it does not only advance the analysis of existing LCS but also puts forward the design of new LCS within that same framework.

Part I. Knowledge representation

• 1. Analyzing Parameter Sensitivity and Classifier Representations for Real-valued XCS
  by Atsushi Wada, Keiki Takadama, Katsunori Shimohara, and Osamu Katai
  4399 - 001
• 2. Use of Learning Classifier System for Inferring Natural Language Grammar
  by Olgierd Unold and Grzegorz Dabrowski
  4399 - 018
• 3. Backpropagation in Accuracy-based Neural Learning Classifier Systems
  by Toby O’Hara and Larry Bull
  4399 - 026
• 4. Binary Rule Encoding Schemes: A Study Using The Compact Classifier System
  by Xavier Llorà, Kumara Sastry , and David E. Goldberg
  4399 - 041

Part II. Mechanisms

• 5. Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System
  by Jaume Bacardit and Josep Maria Garrell
  4399 - 061
• 6. Post-processing Clustering to Decrease Variability in XCS Induced Rulesets
  by Flavio Baronti, Alessandro Passaro, and Antonina Starita
  4399 - 081
• 7. LCSE: Learning Classifier System Ensemble for Incremental Medical Instances
  by Yang Gao, Joshua Zhexue Huang, Hongqiang Rong, and Da-qian Gu
  4399 - 094
• 8. Effect of Pure Error-Based Fitness in XCS
  by Martin V. Butz , David E. Goldberg, and Pier Luca Lanzi
  4399 - 105
• 9. A Fuzzy System to Control Exploration Rate in XCS
  by Ali Hamzeh and Adel Rahmani
  4399 - 116
• 10. Counter Example for Q-bucket-brigade under Prediction Problema
  by Atsushi Wada, Keiki Takadama, and Katsunori Shimohara
  4399 - 130
• 11. An Experimental Comparison between ATNoSFERES and ACS
  by Samuel Landau, Olivier Sigaud, Sébastien Picault, and Pierre Gérard
  4399 - 146
• 12. The Class Imbalance Problem in UCS Classifier System: A Preliminary Study
  by Albert Orriols-Puig and Ester Bernadó-Mansilla
  4399 - 164
• 13. Three Methods for Covering Missing Input Data in XCS
  by John H. Holmes, Jennifer A. Sager, and Warren B. Bilker
  4399 - 184

Part III. New Directions

• 14. A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients
  by Javier G. Marín-Blázquez and Sonia Schulenburg
  4399 - 197
• 15. Adaptive value function approximations in classifier systems
  by Lashon B. Booker
  4399 - 224
• 16. Three Architectures for Continuous Action
  by Stewart W. Wilson
  4399 - 244
• 17. A Formal Relationship Between Ant Colony Optimizers and Classifier Systems
  by Lawrence Davis
  4399 - 263
• 18. Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis
  by John H. Holmes
  4399 - 276

Part IV. Application-oriented research and tools

• 19. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
  by Jaume Bacardit and Martin V. Butz
  4399 - 290
• 20. Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
  by Jaume Bacardit, David E. Goldberg, and Martin V. Butz
  4399 - 299
• 21. Using XCS to Describe Continuous-Valued Problem Spaces
  by David Wyatt, Larry Bull, and Ian Parmee
  4399 - 318
• 22. The EpiXCS Workbench: A Tool for Experimentation and Visualization
  by John H. Holmes and Jennifer A. Sager
  4399 - 343

The book “Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications” edited by Martin Pelikan, Kumara Sastry, and Erick Cantu-Paz has just been published by Springer.

Estimation of distribution algorithms combine evolutionary computation and machine learning to provide a class of robust and scalable optimization techniques applicable to broad classes of difficult problems. Scalable optimization via Probabilistic Modeling compiles articles by some of the leading experts in academia and industry that range from design and analysis to efficiency enhancement and real-world applications of estimation of distribution algorithms. The book is written for the general audience and should be of interest for optimization researchers and practitioners alike.A sample chapter can be downloaded here and more Information can be found at http://medal.cs.umsl.edu/scalable-optimization-book/

• 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.

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.

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.

So far, these are the raw numbers for 2003 and 2004 contributions:

• 2003: 11 chapters by 26 different authors
• 2004: 8 chapters by 15 different authors

The decisions about 2005 will be out soon. We will keep you posted

The deadline for the camera-ready of your contribution to IWLCS was initially set to November 15. Due to the previous delay, we would extend this deadline until November 25 for your convenience. Please do not to hesitate to get in touch if you may not be able to reach this deadline. Due to the size of this volume, we would like to stick to this deadline to be able to have the volume ready for the next workshop edition in Seattle.

For further instructions about how to prepare your camera ready please check the Springer format instructions for authors at

• Springer instructions for authors
• Series information

Contributions should not exceed 20 pages. Authors providing camera- readies that do not complain with the LNCS format or exceed the maximum number of pages will be ask to resubmit them, and may not be included if time constraints do not allow us to do so.

This carefully edited book by Ashish Ghosh and Lakhmi C. Jainreflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. Evolutionary Computation in Data Mining provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics.

This carefully edited book by Larry Bull brings together a fascinating selection of applications of Learning Classifier Systems (LCS). The book demonstrates the utility of this machine learning technique in recent real-world applications in such domains as data mining, modelling and optimization, and control. It shows how the LCS technique combines and exploits many Soft Computing approaches into a single coherent framework to produce an improved performance over other approaches.