On the effect of function set to the generalisation of symbolic regression models.

Supervised learning by means of Genetic Programming aims at the evolutionary synthesis of a model that achieves a balance between approximating the target function on the training data and generalising on new data. In this study we benchmark the approximation / generalisation of models evolved using different function set setups, across a range of symbolic regression problems. Results show that Koza's protected division and power should be avoided, and operators such as analytic quotient and sine should be used instead.