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.
|Publisher:||Springer Berlin Heidelberg|
|Series:||Studies in Fuzziness and Soft Computing Series , #191|
|Edition description:||Softcover reprint of hardcover 1st ed. 2006|
|Product dimensions:||6.10(w) x 9.00(h) x 0.70(d)|
Table of Contents