Advertisement

Table of contents

  1. Front Matter
  2. Jonathan E. Rowe
    Pages 19-43
  3. Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
    Pages 91-125
  4. Jeremy Wyatt
    Pages 177-202
  5. Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai
    Pages 285-304
  6. Anthony J. Bagnall, Zhanna V. Zatuchna
    Pages 305-316
  7. Tim Kovacs, Manfred Kerber
    Pages 317-336

About this book

Introduction

This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.

Keywords

Adaptation Mathematica algorithm algorithms calculus classification complexity dynamics evolution evolutionary computation genetic algorithms knowledge learning machine learning reinforcement learning

Bibliographic information

  • DOI https://doi.org/10.1007/b100387
  • Copyright Information Springer-Verlag Berlin/Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-25073-9
  • Online ISBN 978-3-540-32396-9
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
Industry Sectors
Materials & Steel
Automotive
Chemical Manufacturing
Biotechnology
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering