Learning Probabilistic Tree Grammars for Genetic Programming

Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled algorithms called Estimation–of– Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problem’s structure during optimization. Here, we investigate the use of a specific EDA for GP. We develop a probabilistic model that employs transformations of production rules in a context–free grammar to represent local structures. The results of performing experiments on two benchmark problems demonstrate the feasibility of the approach.