Pier Luca Lanzi just sent a note saying saying that the proceedings of the 2009 Symposium on Computational Intelligence and Games (CIG-2009) are now available on-line at
http://www.ieee-cig.org/cig-2009/Proceedings/
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Drew Mellor is pleased to announce the publication of his new LCS book. Reinforcement Learning, Logic and Evolutionary Computation: A Learning Classifier System Approach to Relational Reinforcement Learning, published by Lambert Academic Publishing (ISBN 978-3-8383-0196-9).
Abstract Reinforcement learning (RL) consists of methods that automatically adjust behaviour based on numerical rewards and penalties. While use of [...]

Learning Classifier Systems:
10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Papers
Series: Lecture Notes in Computer Science
Subseries: Lecture Notes in Artificial Intelligence , Vol. 4998
Bacardit, J.; Bernadó-Mansilla, E.; Butz, M.V.; Kovacs, T.; Llorà, X.; Takadama, K. (Eds.)
2008, X, 307 p., Softcover
ISBN: 978-3-540-88137-7

Abstract
This book [...]

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 [...]

Entrepreneurial times call for The Entrepreneurial Engineer
In an age when technology and business are merging as never before, today’s engineers need skills matched with the times. Today, career success as an engineer is determined as much by an ability to communicate with coworkers, sell ideas, and manage time as by talent at manipulating a Laplace [...]

Reviews from amazon.com:
David Goldberg’s Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field–he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms–and his deep understanding [...]

“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 [...]

This book focuses like a laser beam on one of the hottest topics in evolutionary computation over the last decade or so: estimation of distribution algorithms (EDAs). EDAs are an important current technique that is leading to breakthroughs in genetic and evolutionary computation and in optimization more generally. I’m putting Scalable Optimization via Probabilistic Modeling [...]

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 [...]

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 [...]

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 [...]

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 [...]