Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design / Edition 1

Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design / Edition 1

by Martin V. Butz
ISBN-10:
3642064779
ISBN-13:
9783642064777
Pub. Date:
11/23/2010
Publisher:
Springer Berlin Heidelberg
Select a Purchase Option (Softcover reprint of hardcover 1st ed. 2006)
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Overview

Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design / Edition 1

This 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.

Product Details

ISBN-13: 9783642064777
Publisher: Springer Berlin Heidelberg
Publication date: 11/23/2010
Series: Studies in Fuzziness and Soft Computing Series , #191
Edition description: Softcover reprint of hardcover 1st ed. 2006
Pages: 259
Product dimensions: 6.10(w) x 9.00(h) x 0.70(d)

Table of Contents

Prerequisites.- Simple Learning Classifier Systems.- The XCS Classifier System.- How XCS Works: Ensuring Effective Evolutionary Pressures.- When XCS Works: Towards Computational Complexity.- Effective XCS Search: Building Block Processing.- XCS in Binary Classification Problems.- XCS in Multi-Valued Problems.- XCS in Reinforcement Learning Problems.- Facetwise LCS Design.- Towards Cognitive Learning Classifier Systems.- Summary and Conclusions.

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