Improving Generalization in the XCSF Classifier System Using 
Linear Least-Squares

Daniele Loiacono

XCSF is an extension of XCS in which classifier prediction is
computed as a linear combination of classifier inputs and a
weight vector associated to each classifier. XCSF can adjust
the weight vector of classifiers to evolve accurate piecewise
linear approximations of functions. The Widrow-Hoff rule,
used to update the weight vectors, prevents (when some
conditions hold) XCSF from exploiting the expected piecewise
linear approximation. In this paper we replace the
Widrow-Hoff rule with linear least-squares and we show that
with this improvement XCSF can fully exploit its generalization
capabilities.