Foundations of Learning Classifier Systems

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.

List of contents

Section 1 - Rule Discovery. Population Dynamics of Genetic Algorithms. Approximating Value Functions in Classifier Systems. Two Simple Learning Classifier Systems. Computational Complexity of the XCS Classifier System. An Analysis of Continuous-Valued Representations for Learning Classifier Systems.- Section 2 - Credit Assignment. Reinforcement Learning: a Brief Overview. A Mathematical Framework for Studying Learning Classifier Systems. Rule Fitness and Pathology in Learning Classifier Systems. Learning Classifier Systems: A Reinforcement Learning Perspective. Learning Classifier Systems with Convergence and Generalization.- Section 3 - Problem Characterization. On the Classification of Maze Problems. What Makes a Problem Hard?

Summary

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.

Product details

• Assisted by: Larr Bull (Editor), Larry Bull (Editor), Kovacs (Editor), Kovacs (Editor), Tim Kovacs (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 08.10.2010
• EAN: 9783642064135
• ISBN: 978-3-642-06413-5
• No. of pages: 336
• Dimensions: 156 mm x 234 mm x 18 mm
• Weight: 521 g
• Illustrations: VI, 336 p.
• Series: Studies in Fuzziness and Soft Computing; Studies in Fuzziness and Soft Computing
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; C, Künstliche Intelligenz, machine learning, Artificial Intelligence, DV-gestützte Biologie/Bioinformatik, Angewandte Mathematik, Knowledge, Learning, molecular biology, bioinformatics, engineering, Reinforcement Learning, Applications of Mathematics, Mathematical and Computational Engineering, Engineering mathematics, Applied mathematics, Information technology: general issues, Mathematical and Computational Engineering Applications