Abstract
Learning Classifier Systems have previously been shown to have some application in deducing the characteristics of complex multi-modal test environments to a suitable level of accuracy. In this study, the issue of presenting human-readable rulesets to a potential user is addressed. In particular, two existing ruleset compaction algorithms originally devised for rulesets with an integer-valued representation are applied to rulesets with a continuous-valued representation. The algorithms are used to reduce the size of rulesets evolved by the XCS classifier system. Following initial testing, both algorithms are modified to take into account problems associated with the new representation. Finally, the modified algorithms are shown to outperform the originals.
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Wyatt, D., Bull, L., Parmee, I.C. (2004). Building Compact Rulesets for Describing Continuous-Valued Problem Spaces Using a Learning Classifier System. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_20
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DOI: https://doi.org/10.1007/978-0-85729-338-1_20
Publisher Name: Springer, London
Print ISBN: 978-1-85233-829-9
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