Smooth Fitting with a Method for Determining the Regularization Parameter under the Genetic Programming Algorithm

Abstract

This paper deals with the smooth fitting problem under the genetic programming algorithm(GP). To reduce the computational cost required for evaluating the fitness value of GP trees, numerical weights of GP trees are estimated by adopting both linear associative memories and the Hook & Jeeves method. The quality of smooth fitting is critically dependent on the choice of the regularization parameter. So, we present a novel method for choosing regularization parameter. Two numerical examples are given with the comparison of generalized cross-validation B-splines.