  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.
