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

rule-based evolutionary online learning systems : a principled approach to lcs analysis and design (studies in fuzziness and soft computing)

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

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Book Description
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.

Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design (Studies in Fuzziness and Soft Computing),Martin V. Butz,Springer,3540253793,Applied,Artificial Intelligence - General,Computer Books: Internet General,Computers,Internet - General,Mathematics,Neuroscience,Artificial Intelligence / Machine Learning,Cognitive Science,Genetic Algorithms,Genetics-Based Machine Learning,Learning Classifier Systems,Mathematics / Applied,Online Learning,Reinforcement Learning

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