Rule-Based Evolutionary Online Learning Systems - A Principled Approach to LCS Analysis and Design (Hardcover, 2006 ed.)

Rule-basedevolutionaryonlinelearningsystems, oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces, andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis, understanding, anddesign;(2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland's originally envisioned cognitive systems. Martin V.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Fuzziness and Soft Computing, 191
Release date:	November 2005
First published:	2006
Authors:	Martin V. Butz
Dimensions:	297 x 210 x 23mm (L x W x T)
Format:	Hardcover
Pages:	259
Edition:	2006 ed.
ISBN-13:	978-3-540-25379-2
Categories:	Books > Computing & IT > General theory of computing > General Books > Computing & IT > Applications of computing > General Promotions
LSN:	3-540-25379-3
Barcode:	9783540253792

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