Function approximation is an important technique used in many different domains, including numerical mathematics, engineering, and neuroscience. The XCSF classifier system is able to approximate complex multi-dimensional function surfaces using a patchwork of simpler functions. Typically, locally linear functions are used due to the tradeoff between expressiveness and interpretability. This work discusses XCSF’s current capabilities, but also points out current challenges that can hinder learning success. A theoretical discussion on
when XCSF works
is intended to improve the comprehensibility of the system. Current advances with respect to
theory show that the system constitutes a very effective machine learning technique. Furthermore, the paper points-out how to tune relevant XCSF parameters in actual applications and how to choose appropriate condition and prediction structures. Finally, a brief comparison to the Locally Weighted Projection Regression (LWPR) algorithm highlights positive as well as negative aspects of both methods.