Extending XCSF beyond linear approximation

XCSF is the 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 exploit classifiers’ computable prediction to evolve accurate piecewise linear approximations of functions. In this paper, we take XCSF one step further and show how XCSF can be easily extended to allow polynomial approximations. We test the extended version of XCSF on various approximation problems and show that quadratic/cubic approximations can be used to significantly improve XCSF’s generalization capabilities. Categories and Subject Descriptors

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wilson, David E. Goldberg
• Keywords: Computable Prediction, GECCO 2005, Piecewise Linear Approximations, Polynomial Approximations