Classifiers that approximate functions
Natural Computing: an international journal
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Classifier fitness based on accuracy
Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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The XCS classifier system has been successfully applied to various problem domains including datamining, boolean classifications, and function approximation. In all these applications just two classifiers were reproduced in a match or action set, given a time-recency threshold was met in the set. In this paper, we investigate the effect of selecting more than two classifiers for reproduction in XCSF. We either increase the number of selected classifiers or select a number of classifiers relative to the current match set size. In the functions investigated, both approaches showed a highly significant increase in initial learning speed. Also, in less challenging approximation tasks, the final accuracy reached is not affected by the approach. However, in harder functions, learning may stall due to over-reproductions of inaccurate, ill-estimated classifiers. Thus, we propose an adaptive offspring size rate that may depend on the current reliability of classifier parameter estimates. First results with a fixed offspring set size decrement show promising results. Future work is needed to speed-up XCS's learning progress and adjust its learning speed to the perceived problem difficulty.