Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A key strength of GP-GOMEA is its way of performing variation, which dynamically adapts to the emergence of patterns in the population. However, GP-GOMEA lacks a mechanism to optimize coefficients. In this paper, we study how fairly simple approaches for optimizing coefficients can be integrated into GP-GOMEA. In particular, we considered two variants of Gaussian coefficient mutation. We performed experiments using different settings on 23 benchmark problems, and used machine learning to estimate what aspects of coefficient mutation matter most. We find that the most important aspect is that the number of coefficient mutation attempts needs to be commensurate with the number of mixing operations that GP-GOMEA performs. We applied GP-GOMEA with the best-performing coefficient mutation approach to the data sets of SR-Bench, a large SR benchmark, for which a ground-truth underlying equation is known. We find that coefficient mutation can help rediscovering the underlying equation by a substantial amount, but only when no noise is added to the target variable. In the presence of noise, GP-GOMEA with coefficient mutation discovers alternative but similarly-accurate equations.

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2022 Genetic and Evolutionary Computation Conference, GECCO 2022
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Virgolin, M, & Bosman, P.A.N. (2022). Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 2289–2297). doi:10.1145/3520304.3534036