Rule-based evolutionary online learning systems : a principled approach to LCS analysis and design / Martin V. Butz.
"Rule-basedevolutionaryonlinelearningsystems,oftenreferredtoas Michig- 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-...
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| Main Author: | |
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| Format: | Ebook |
| Language: | English |
| Published: |
Berlin :
Springer,
[2006]
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| Series: | Studies in fuzziness and soft computing ;
v. 191. |
| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | "Rule-basedevolutionaryonlinelearningsystems,oftenreferredtoas Michig- 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 - proachfor LCSsthatpromotessystemanalysis,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."--Publisher's website. |
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| Physical Description: | 1 online resource (xxi, 266 pages) : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 3540253793 9783540253792 1280427493 9781280427497 3540312315 9783540312314 |