Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design (Studies in Fuzziness and Soft Computing) (Hardcover)

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 Hollands original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.