Solving the Exponential Growth of Symbolic Regression Trees in Geometric Semantic Genetic Programming
This addresses a critical bottleneck in GSGP for symbolic regression, enabling more practical use of solutions by drastically reducing computational overhead.
The paper tackles the exponential growth of symbolic regression trees in Geometric Semantic Genetic Programming by introducing GSGP-Red, a method that simplifies individuals through expansion and aggregation, reducing node counts by 58 orders of magnitude on average across 12 real-world datasets.
Advances in Geometric Semantic Genetic Programming (GSGP) have shown that this variant of Genetic Programming (GP) reaches better results than its predecessor for supervised machine learning problems, particularly in the task of symbolic regression. However, by construction, the geometric semantic crossover operator generates individuals that grow exponentially with the number of generations, resulting in solutions with limited use. This paper presents a new method for individual simplification named GSGP with Reduced trees (GSGP-Red). GSGP-Red works by expanding the functions generated by the geometric semantic operators. The resulting expanded function is guaranteed to be a linear combination that, in a second step, has its repeated structures and respective coefficients aggregated. Experiments in 12 real-world datasets show that it is not only possible to create smaller and completely equivalent individuals in competitive computational time, but also to reduce the number of nodes composing them by 58 orders of magnitude, on average.