Abstract
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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© 2006 Springer
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Butz, M.V. (2006). XCS in Multi-Valued Problems. In: Rule-Based Evolutionary Online Learning Systems. Studies in Fuzziness and Soft Computing, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31231-5_9
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DOI: https://doi.org/10.1007/3-540-31231-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25379-2
Online ISBN: 978-3-540-31231-4
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