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.
Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design (Studies in Fuzziness and Soft Computing)
The book 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 … Continue reading →
The book 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.
Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design (Studies in Fuzziness and Soft Computing)
The book 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 […]
The book 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.
Ninth International Workshop on Learning Classifier Systems (IWLCS 2006) – CFP
Seattle, WA, USA, July 8-9, 2006. To be held during the Genetic and Evolutionary Computation Conference (GECCO-2006), July 8-12, 2006.
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 2006.
Submissions
There are two possibilities for paper submissions. Both will be peer reviewed, but reviews of short papers will be mainly to provide feedback to authors – we expect most or all will be accepted.
1) Short papers of up to 4 pages may be submitted. Accepted short papers will be presented at the workshop and published in the GECCO workshop volume. The format of the GECCO workshop volume is to be confirmed but we expect it will be the ACM format used in 2005. After the workshop authors will be invited to submit full papers which are reviewed again for the post-workshop proceedings, which we plan to publish in Springer’s LNAI series as in past years.
2) Full papers of up to 20 pages (in Springer format) may be submitted for peer review before the workshop. Accepted full papers will be presented at the workshop and will be published in the post-workshop proceedings. Authors of full papers have a choice of how to contribute to the GECCO workshop volume: either i) prepare a short version for GECCO or ii) publish only your abstract in the GECCO book. If you prefer i) we would suggest an extended abstract of 1 or 2 pages, but anything up to 50% of the full paper is ok.
Papers should be submitted as PDF files e-mailed to iwlcs@cas.dis.titech.ac.jp.
Important dates
Please note: all dates are to be confirmed.
- Paper submission deadline: March 24, 2006 (extended)
- Decisions: April 12, 2006 (extended)
- GECCO 2006 Workshop proceedings camerar-ready: April 26, 2006 (extended)
- Workshop: July 8-9, 2006
Camera Ready for GECCO 2006 Workshop Proceedings
The camera-ready papers should be formated following the instructions provided by GECCO. Failing to comply will result in exclusion from the proceedings. The proceedings will only be published on CD-ROM. Camera-ready papers must be submitted using the GECCO-2006 Submission & Review site at https://ssl.linklings.net/conferences/gecco2006/.
Organization
Organizing Commitee
- Tim Kovacs, University of Bristol (UK)
- Xavier Llorà, University of Illinois at Urbana-Champaign (USA)
- Keiki Takadama, Tokyo Institute of Technology (Japan)
Advisory Committee
- Pier Luca Lanzi, Politechnico de Milano (Italy)
- Wolfgang Stolzmann, Daimler Chrysler AG (Germany)
- Stewart Wilson, Prediction Dynamics (USA)
Program Committee
- Bacardit, Jaume. University of Nottingham (UK)
- Bagnall, Tony. Univesity of East Anglia (UK)
- Barry, Alwyn. University of Bath (UK)
- Bernadó Mansilla, Ester. Universitat Ramon Llull (Spain)
- Bonarini, Andrea. Politecnico di Milano (Italy)
- Booker, Lashon. The Mitre Corporation (USA)
- Browne, Will. University of Reading (UK)
- Bull, Larry. University of West England (UK)
- Butz, Martin. Universitat Wurzburg (Germany)
- Carse, Brian. University of West England (UK)
- Davis, David. NuTech Solutions (USA)
- Drugowitsch, Jan. University of Bath (UK)
- Egginton, RobUniversity of Bristol (UK)
- Herrera, Francisco. Universidad de Granada (Spain)
- Holmes, John. University of Pennsylvania (USA)
- Homaifar, Abdollah. North Carolina A&T State University (USA)
- Kovacs, Tim. University of Bristol (UK)
- Lanzi, Pier Luca. Politecnico di Milano (Italy)
- Llorà , Xavier. University of Illlinois at Urbana-Champaign (USA)
- Marin-Blazquez, Javier. Universidad de Murcia (Spain)
- Miramontes-Hercog, Luis. Instituto Tecnológico y de Estudios Superiores de Monterrey (Mexico)
- Muruzabal, Jorge. Universidad Rey Juan Carlos (Spain)
- Schulenburg, Sonia. University of Edinburgh (UK)
- Sigaud, Olivier. Laboratoire d’Informatique de Paris 6 (France)
- Stolzman, Wolfgang. Daimler Chrysler AG (Germany)
- Takadama, Keiki. Tokyo Institute of Technology (Japan)
- Wada, Atsushi. Advanced Telecomunications Research Institute (Japan)
- Wilson, Stewart. Prediction Dynamics (USA)
- Zatuchna, Z. V. Univesity of East Anglia (UK)
For further information please contact iwlcs@cas.dis.titech.ac.jp.
Advances at the frontier of LCS (Volume I) is coming
The 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
Springer 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.
Hierarchical Bayesian Optimization Algorithm : Toward a New Generation of Evolutionary Algorithms (Studies in Fuzziness and Soft Computing)
Book Description Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is … Continue reading →
Book Description Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA) . They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search, motivates and describes BOA and hBOA in a way accessible to a wide audience and presents numerous results confirming that they are revolutionary approaches to black-box optimization.
Hierarchical Bayesian Optimization Algorithm : Toward a New Generation of Evolutionary Algorithms (Studies in Fuzziness and Soft Computing)
Book Description Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling […]
Book Description Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA) . They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search, motivates and describes BOA and hBOA in a way accessible to a wide audience and presents numerous results confirming that they are revolutionary approaches to black-box optimization.
Eighth International Workshop on Learning Classifier Systems (IWLCS 2005) – CFP
Washington, D.C., USA, June 25, 2005. To be held during the Genetic and Evolutionary Computation Conference (GECCO-2005), June 25-29, 2005.
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 currently enjoying a renaissance, 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. Two free tutorials on LCS will be presented at GECCO 2005: an introductory tutorial on LCS in general and an advanced tutorial on XCS.
Special Session on Challenges for the Field
What are the key challenges, issues, limitations, technologies and directions for LCS research? We plan a special session consisting of 5 minute presentations on challenges for the field and how to solve them. If interested, please send your name and title of your presentation to the organisers by April 10, 2005.
Submissions
Accepted papers will appear in the GECCO 2005 workshop volume distributed at the conference, and, as in previous years, we intend to publish selected papers in a post-workshop proceedings in Springer’s LNAI series.
There are two possibilities for paper submissions. Short papers of up to 4 pages may be submitted. Short papers will be peer reviewed and those accepted will be presented at the workshop. After the workshop the authors will have to submit full papers which are reviewed again for the post-workshop proceedings.
Alternatively, full papers of up to 20 pages may be submitted for peer review before the workshop. All accepted full papers will be presented at the workshop and will be published in the post-workshop proceedings.
All papers should be in LNAI format. A PDF file containing the paper should be e-mailed to iwlcs@cas.dis.titech.ac.jp by the 10th of April 2005.
Camera ready for GECCO’2005 workshop proceedings
Accepted short papers will be published in the GECCO workshop book. Authors of long papers have a choice: either i) prepare a short version for GECCO or ii) send them only your abstract for the GECCO book. If you prefer i) I would suggest an extended abstract of 1 or 2 pages, but anything up to 75% of the full paper is ok.
All papers need to be format following the ACM camera-ready directives of all accepted workshop papers should be submitted by April 26 for their inclusion in the GECCO 2005 workshop proceedings. It is important to note that those papers needs to be format following ACM directives. Further instructions for the preparation of the workshop papers can be found at http://www.sheridanprinting.com/typedept/gecco1.htm.
Remember to include the proper ACM conference information
\conferenceinfo{GECCO'05,} {June 25--29, 2005, Washington, DC, USA.}
\CopyrightYear{2005}
\crdata{1-59593-097-3/05/0006}
Important dates
- Paper submission deadline: April 10, 2005 (Deadline extended)
- Decisions: April 21, 2005
- GECCO 2005 Workshop proceedings camerar-ready: April 26, 2005
- Workshop: June 25, 2005
Organization
Organizing Commitee
- Tim Kovacs, University of Bristol
- Xavier Llorà, University of Illinois at Urbana-Champaign,
- Keiki Takadama, Tokyo Institute of Technology
Advisory Committee
- Pier Luca Lanzi, Politechnico de Milano
- Wolfgang Stolzmann, DaimlerChrysler AG, Berlin
- Stewart Wilson, Prediction Dynamics, Concord. MA
Program Committee
- Alwyn Barry,University of Bath
- Andrea Bonarini, Politecnico di MIlano
- Lashon Booker, The MITRE Corporation
- Will Browne, The University of Reading
- Larry Bull, University of West England
- Martin Butz, University of Wuerzburg
- Rob Egginton, University of Bristol
- Pierre Gérard, University of Paris 13
- John Holmes, University of Pennsylvania School of Medicine
- Jacob Hurst, University of West England
- Sonia Schulenburg, Napier University
- Olivier Sigaud, University of Paris 6
- Jaume Bacardit, University of Nottingham
- Ester Bernadó-Mansilla, Ramon Llull University
- Luis Miramontes Hercog
- Christopher Stone, University of the West of England
- Atsushi Wada, ATR Network Informatic Laboratories
For further information please contact iwlcs@cas.dis.titech.ac.jp.
Evolutionary Computation in Data Mining
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.