Towards increasing learning speed and robustness of XCSF: experimenting with larger offspring set sizes

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.