  The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based
EA framework that has been shown to perform well in several domains, including
Genetic Programming (GP). Differently from traditional EAs where variation acts
blindly, GOMEA learns a model of interdependencies within the genotype, i.e.,
the linkage, to estimate what patterns to propagate. In this article, we study
the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression
(SR). We show that the non-uniformity in the distribution of the genotype in GP
populations negatively biases LL, and propose a method to correct for this. We
also propose approaches to improve LL when ephemeral random constants are used.
Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of
tuning the population size, a crucial parameter for LL, to SR. We run
experiments on 10 real-world datasets, enforcing a strict limitation on
solution size, to enable interpretability. We find that the new LL method
outperforms the standard one, and that GOMEA outperforms both traditional and
semantic GP. We also find that the small solutions evolved by GOMEA are
competitive with tuned decision trees, making GOMEA a promising new approach to
SR.

Neural and Evolutionary Computing