  The application of Genetic Programming to the discovery of empirical laws is
often impaired by the huge size of the search space, and consequently by the
computer resources needed. In many cases, the extreme demand for memory and CPU
is due to the massive growth of non-coding segments, the introns. The paper
presents a new program evolution framework which combines distribution-based
evolution in the PBIL spirit, with grammar-based genetic programming; the
information is stored as a probability distribution on the gra mmar rules,
rather than in a population. Experiments on a real-world like problem show that
this approach gives a practical solution to the problem of intron growth.
