Probabilistic incremental program evolution: Stochastic search through program space

Abstract

Probabilistic Incremental Program Evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions [Schmidhuber, 1997], Population- Based Incremental Learning (PBIL) [Baluja and Caruana, 1995] and tree-coding of programs used in variants of Genetic Programming (GP) [Cramer, 1985; Koza, 1992]. PIPE uses a stochastic selection method for successively generating better and better programs according to an adaptive “probabilistic prototype tree”. No crossover operator is used. We compare PIPE to Koza’s GP variant on a function regression problem and the 6-bit parity problem.