Titlebook: Rule-Based Evolutionary Online Learning Systems; A Principled Approac Martin V. Butz Book 2006 Springer-Verlag Berlin Heidelberg 2006 Artif

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XCS in Reinforcement Learning Problems,zing and specificity, as well as the dependence on problem properties such as the minimal order complexity, infrequent niche occurrences, or overlapping solution representations. The datamining applications confirmed XCS’s machine learning competitive learning behavior in terms of accuracy and solution generality.

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Book 2006olland, 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 in

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XCS in Multi-Valued Problems,This chapter investigates XCS’s performance in various real- and/or nominal valued datasets as well as in function approximation problems. The application to problems other than binary valued ones requires a modification of the XCS classifier system condition parts as well as its genetic operators including covering, mutation, and crossover.

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978-3-642-06477-7Springer-Verlag Berlin Heidelberg 2006

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