P-Tree Programming

TL;DR

P-Tree Programming introduces a probabilistic approach for automatic program synthesis using a prototype tree, outperforming genetic programming on symbolic regression benchmarks with a concise parameter set.

Contribution

It presents a novel probabilistic method for program synthesis that efficiently explores the search space through a prototype tree, improving performance over standard genetic programming.

Findings

Outperforms standard Genetic Programming on symbolic regression benchmarks

Uses a concise set of parameters applicable across various problems

Applicable to classification, program induction, and symbolic regression

Abstract

We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagated through the prototype tree. We use them to update the probability distributions that determine the symbol choices of further instances. The iterative method is applied to several symbolic regression benchmarks from the literature. It outperforms standard Genetic Programming to a large extend. Furthermore, it relies on a concise set of parameters which are held constant for all problems. The algorithm can be employed for most of the typical computational intelligence tasks such as classification, automatic program induction, and symbolic regression.