LCS & GBML Central: Community resource is now Online
March 27th, 2009
LCSweb 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
August 1st, 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.
Alwyn Barry changing jobs
September 19th, 2007
This is an excerpt from Alwyn Barry’s web page (Thanks Pier Luca for pointing it out)
Most people who know me will be aware that I am changing job shortly. I will be leaving the University of Bath from 30th September 2007, and will no longer be an academic. I am moving to Street in Somerset to become the Pastor of Street Baptist Church. I am still happy to answer any questions relating to my previous research, so do feel free to contact me via my new email address, which is linked from this site.
Alwyn, it has been a pleasure to be able to interact with you. I would like to wish you the best in your new endeavor, knowing that you will give your 100% as usual.
E2K blog has moved
August 2nd, 2007
The E2K blog has moved. You can reach it at
http://dita.ncsa.uiuc.edu/e2k/
XCSFJava 1.1
August 1st, 2007
XCSFJava 1.1 code authored by Martin V. Butz was posted online and its documentation was posted as Missouri Estimation of Distribution Algorithms Laboratory Technical Report No. 2007008. Click here to access MEDAL software, or here to access MEDAL reports.
IWLCS 2007 - Extended Deadline
March 19th, 2007
The deadline for workshop submissions has been extended until friday, the 23rd of March. Later submissions may be considered for presentation but will not appear in the conference workshop proceedings. For further information, please consult the IWLCS 2007 webpage.
International Workshop on Learning Classifier Systems (IWLCS 2007)
January 17th, 2007
The Tenth International Workshop on Learning Classifier Systems (IWLCS 2007)
will be held on July 7th or 8th, 2007 in association with the conference The Genetic and Evolutionary Computation Conference: GECCO 2007 held at the University College London, in London, England.
Post-workshop proceedings will be published in Springer’s Lecture Notes in Computer Science / Artificial Intelligence series (LNCS/LNAI).
The call For Papers is available here.
Submission deadline is March, 16th.
Call For Papers: The Tenth International Workshop on Learning Classifier Systems (IWLCS 2007)
January 17th, 2007
Call for Papers for IWLCS 2007
The Tenth International Workshop on Learning Classifier Systems (IWLCS 2007) will be held in London, UK, July 7-8, 2007 during the Genetic and Evolutionary Computation Conference (GECCO-2007), July 7-11, 2007.
Since Learning Classifier Systems (LCSs) were introduced by John H. Holland as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining, to automated innovation, and to the on-line control of cognitive systems. LCS is a very active area of research that encompasses various system approaches. Wilson’s accuracy-based XCS system has received the highest attention and gained the highest reputation.
LCSs are benefiting from recent advances in machine learning, and reinforcement learning in particular, as well as in evolutionary computation. Novel insights in these two areas are continuously integrated into the LCS framework.
We invite submissions which discuss recent developments in all areas of research on, and applications of, Learning Classifier Systems. IWLCS is the event that brings together most of the core researchers in classifier systems. Moreover, a free introductory tutorial on LCSs is presented at GECCO 2007. The IWLCS workshop gives the opportunity also to researchers interested in LCS to get an impression of the current research directions in the field.
Submissions and Publication
There are two ways to submit papers (deadline March 16, 2007):
- short papers (up to 4 pages in ACM format) or
- full papers (up to 20 pages in Springer format)
All accepted papers may be presented orally at IWLCS. Accepted short papers will appear in the GECCO workshop volume. Proceedings of the workshop will be published on CD-ROM, and distributed at the conference. Authors of short papers will be invited after the workshop to submit revised (full) papers for publication in the post-workshop proceedings, in Springer LNCS/LNAI book series.
Accepted full papers will be published in the post-workshop proceedings. Authors of accepted full papers will be asked to provide a shorter 4-pages version for publication in the GECCO 2007 workshop proceedings.
The normal route is for authors to submit short papers and produce full papers after IWLCS for the post-workshop proceedings, incorporating feedback from reviewers and delegates. All submissions will be peer reviewed. Reviews of short papers will be mainly to provide feedback to enable the production of an improved full paper.
All papers should be submitted in PDF format and e-mailed to: esterb@salle.url.edu.
Important dates
- Paper submission deadline: Friday, March 16, 2007
- Notification to authors: Friday, March 30, 2007
- GECCO camera-ready material: by Wednesday, April 11, 2007
- Conference registration: Wednesday, April 11, 2007
- Workshop date: 7th or 8th July
- Extended paper submissions for LNCS/LNAI post-workshop proceedings: early fall 2007
- Notification of acceptance: late fall 2007
- LNCS/LNAI camera ready material: winter 2007/08
Committees
Organizing Commitee
- Jaume Bacardit, University of Nottingham (UK). E-mail: jqb@cs.nott.ac.uk
- Ester Bernadó-Mansilla, Universitat Ramon Llull (Spain). E-mail: esterb@salle.url.edu
- Martin V. Butz, Universitat Wurzburg (Germany). E-mail: mbutz@psychologie.uni-wuerzburg.de
Advisory Committee
- Tim Kovacs, University of Bristol (UK).
- Xavier Llorà, University of Illinois at Urbana-Champaign (USA).
- Pier Luca Lanzi, Politechnico de Milano (Italy).
- Wolfgang Stolzmann, Daimler Chrysler AG (Germany).
- Keiki Takadama, Tokyo Institute of Technology (Japan).
- Stewart Wilson, Prediction Dynamics (USA).
For more information please check here.
Preliminary IWLCS 2007 CFP
January 10th, 2007
London, UK, July 7-9, 2007. To be held during the Genetic and Evolutionary Computation Conference (GECCO-2007), July 7-11, 2007.
Since Learning Classifier Systems (LCSs) were introduced by Holland as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining to automated innovation to on-line control. Classifier systems are a very active area of research, with newer approaches, in particular Wilson’s accuracy-based XCS, receiving a great deal of attention. LCS are also benefiting from advances in the field of reinforcement learning, and there is a trend toward developing connections between the two areas. We invite submissions which discuss recent developments in all areas of research on, and applications of, Learning Classifier Systems. IWLCS is the only event to bring together most of the core researchers in classifier systems. A free introductory tutorial on LCS will be presented at GECCO 2007.
The final call for papers can be found here.
Advances at the frontier of LCS: LNCS 4399
January 8th, 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
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