Archive for 'Books' Category
LCS & GBML Central: Community resource is now Online
27 March 2009LCSweb was designed to allow researchers and those seeking to use Learning Classifier Systems within applications access to material on LCS and discussion between members of the LCS community. The site served this community since its was started by Alwyn Barry in 1997. Enhanced and maintained later by Jan Drugowitsch, LCSweb became a valuable community resource. The site was completely community-driven and allowed members to contribute to the content of the site and keeping it up to date. Later on in 2005, I started “LCS and other GBML” Blog to cover a gap providing information information regarding the International Workshop on Learning Classifier Systems (IWLCS), the collection of LCS Books available, and GBML related news.Some of you may have realized that after Jan’s move to Rochester and Alwyn’s retirement from research activities, LCSweb has vanished. Will Browne took on himself to take LCSweb to Reading, but technical circumstances have made that move rocky despite his best efforts. Jan and Will however still have a local copy of LCSweb contents. After talking to Jan and Will, I proposed to merge LCSweb with the LCS and other GBML blog, and host the new site at NCSA where dedicated resources has been made available. Jan and Will agreed with the idea. The current progress merging both sites can be summarized as follows:Done:
- Resources for hosting are available at NCSA
- LCS and other GBML blog has been moved to NCSA (the old one will still be there for historic reasons and no updates will be made to it)
- A new name for the new merged site has been proposed (LCS & GBML Central)
In progress:
- Jan has taken upon himself to start slowly migrating the old content into the new site (be patient, he is doing that manually for which we should be very thank full)
Besides, we have added two extra features to the site
- Automatic aggregation of feeds (some of you may know this as you may have seen in so call planet sites). I just did a quick list of feeds that I knew and added them to the aggregator. Unfortunately, few of the sites of our community provided feeds, so I would encourage everybody to think about it. Why may this be important? The updates of those feeds go straight into LCS & GBML Central. That would make possible to create one stop place for information in the LCS and GBML community, and still maintain each separate member’s identity (you will see that when you click on an aggregated entry, you will be directed to the originators site)
- Added forums to complement the LCS and GBML mailing list. Not to sure how useful will be, but at least it may help to jump in and ask questions (moderators volunteers more than welcome).
As mentioned above, the site is still on the building steps. LCSweb relevant content will be migrated slowly, but the main place holders are already there for your evaluation. Since this is a site for the community, we would love to hear about your feedback and ideas. As soon as new steps are conquered, we will keep you posted. Also, if you would like to help with the site, content transition, or know about related feeds that should be aggregated please drop us an email, and make this community your community.LCS & GBML Central can be reach at
Best,Jan, Will, and Xavier
Design and Analysis of Learning Classifier Systems: A Probabilistic Approach
1 August 2008
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.
Advances at the frontier of LCS: LNCS 4399
8 January 2007“Advances at the frontier of Learning Classifier Systems” has been shipped to Springer for the final stages of editing and printing. The volume is going to be printed as Springer’s LNCS 4399 volume. When we started editing this volume, we faced the choice of organizing the contents in a purely chronological fashion or as a sequence of related topics that help walk the reader across the different areas. In the end we decided to organize the contents by area, breaking a little the time-line. This was not a simple endeavor as we could organize the material using multiple criteria. The taxonomy below is our humble effort to provide a coherent grouping. Needless to say, some works may fall in more than one category. Below, you may find the tentative table of contents of the volume. It may change a little bit, but we will keep you posted as soon as we learn from Springer.
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
Scalable optimization via probabilistic modeling: From algorithms to applications
14 November 2006
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/
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.
-
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.
Advances at the frontier of LCS (Volume I) is coming
1 December 2005The final editing of the volume Advances at the frontier of LCS to be published by Springer is advancing at steady pace. The volume is going to be an overview of the research LCS and other GBML presented at IWLCS. The volume will cover 2003, 2004, and 2005 contributions.
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
Camera ready instructions for IWLCS 2003 and 2004 proceedings
7 November 2005Springer has agreed to publish the compilation volume Advances in Learning Classifier Systems (the title may be slightly changed) including contributions from the International Workshop of Learning Classifier Systems in its editions of 2003, 2004, and 2005. This volume will present an overview of the work presented in the last three years of the workshop and will include up to 30 contributions.
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
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.
Evolutionary Computation in Data Mining
23 November 2004
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.