Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design
Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design By Martin V. Butz
Publisher: Sp rin ger 2005 | 266 Pages | ISBN: 3540253793 | PDF | 5 MB
Publisher: Sp rin ger 2005 | 266 Pages | ISBN: 3540253793 | PDF | 5 MB
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.
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