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Rule-Based Evolutionary Online Learning Systems

A Principled Approach to LCS Analysis and Design

Medium: Buch
ISBN: 978-3-540-25379-2
Verlag: Springer Berlin Heidelberg
Erscheinungstermin: 24.11.2005
Lieferfrist: bis zu 10 Tage

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 cognitivesystems. Martin V.


Produkteigenschaften


  • Artikelnummer: 9783540253792
  • Medium: Buch
  • ISBN: 978-3-540-25379-2
  • Verlag: Springer Berlin Heidelberg
  • Erscheinungstermin: 24.11.2005
  • Sprache(n): Englisch
  • Auflage: 2006
  • Serie: Studies in Fuzziness and Soft Computing
  • Produktform: Gebunden
  • Gewicht: 1290 g
  • Seiten: 259
  • Format (B x H x T): 160 x 241 x 21 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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.