Rule-Based Evolutionary Online Learning Systems [electronic resource] : A Principled Approach to LCS Analysis and Design / by Martin V. Butz.

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.

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.

ZDB-2-ENG