Design and Analysis of Learning Classifier Systems

A Probabilistic Approach

  • Authors
  • Jan Drugowitsch

Part of the Studies in Computational Intelligence book series (SCI, volume 139)

Table of contents

  1. Front Matter
  2. Jan Drugowitsch
    Pages 1-11
  3. Jan Drugowitsch
    Pages 13-28
  4. Jan Drugowitsch
    Pages 29-44
  5. Jan Drugowitsch
    Pages 45-64
  6. Jan Drugowitsch
    Pages 65-99
  7. Jan Drugowitsch
    Pages 101-121
  8. Jan Drugowitsch
    Pages 123-164
  9. Jan Drugowitsch
    Pages 165-201
  10. Jan Drugowitsch
    Pages 203-235
  11. Jan Drugowitsch
    Pages 237-239
  12. Back Matter

About this book


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.


Analysis algorithm algorithms evolution evolutionary computation learning machine learning model reinforcement learning

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-540-79865-1
  • Online ISBN 978-3-540-79866-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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