Inductive genetic programming of polynomial learning networks
Nikolay Alekseevich Nikolaev, Hitoshi Iba · 2002
Learning networks have been empirically proven suitable for function approximation and regression. Our concern is finding well performing polynomial learning networks by inductive Genetic Programming (iGP). The proposed iGP system evolves tree-structured networks of simple transfer polynomials in the hidden units. It discovers the relevant network topology for the task, and rapidly computes the network weights by a least-squares method. We implement evolutionary search guidance by an especially developed fitness function for controlling the overfitting with the examples. This study reports that iGP with the novel fitness function has been successfully applied to benchmark time-series prediction and data mining tasks.