  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.
