Numeric mutation as an improvement to symbolic regression in genetic programming

Abstract

A weakness of genetic programming (GP) is the difficulty it suffers in discovering useful numeric constants for the terminal nodes of the s-expression trees. We examine a solution to this problem, called numeric mutation, based, roughly, on simulated annealing. We provide empirical evidence to demonstrate that this method provides a statistically significant improvement in GP system performance for symbolic regression problems. GP runs are more likely to find a solution, and successful runs use fewer generations.