{"product_id":"rule-based-evolutionary-online-learning-systems-a-principled-approach-to-lcs-analysis-and-design-9783540253792","title":"Rule-Based Evolutionary Online Learning Systems: A Principled Approach to Lcs Analysis and Design","description":"\u003cp\u003e • Author(s): Martin V. Butz\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Applied\u003c\/p\u003e\u003cp\u003eRule-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.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Hardcover","offer_id":47613428564119,"sku":"9783540253792","price":7345.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783540253792.webp?v=1775085116","url":"https:\/\/atlanticbooks.com\/products\/rule-based-evolutionary-online-learning-systems-a-principled-approach-to-lcs-analysis-and-design-9783540253792","provider":"Atlantic Books","version":"1.0","type":"link"}