Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
XCS revisited: a novel discovery component for the eXtended classifier system
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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This paper provides an extension of the rule combining (RC) technique in the Accuracy-based Learning Classifier System (XCS) to handle continuous-valued input. Previously implemented to cope with binaries, the suitability of the newly introduced algorithm is investigated in further tasks. Several experiments are run and the results are compared to previous work using the real-valued multiplexer problem. The comparison shows that by implementing the RC technique, real value XCS is capable of producing a compact population of rules through proper generalizations. Moreover, the learning rate of Real-value XCS-RC (RXCS-RC) is comparable or even superior, in some cases.