Avoiding the Bloat with Stochastic Grammar-based Genetic Programming

TL;DR

This paper introduces a new grammar-based genetic programming framework that uses probability distributions to reduce intron growth and resource consumption in the evolution process.

Contribution

It combines distribution-based evolution with grammar-based genetic programming to effectively control intron growth and resource usage.

Findings

Reduces memory and CPU requirements in genetic programming.

Effectively controls intron growth in evolved programs.

Demonstrates practical benefits on real-world-like problems.

Abstract

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.