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Symbolic regression searches for analytic expressions that accuratelydescribe studied phenomena. The main attraction of this approach is that itreturns an interpretable model that can be insightful to users. Historically,the majority of algorithms for symbolic regression have been based onevolutionary algorithms. However, there has been a recent surge of newproposals that instead utilize approaches such as enumeration algorithms, mixedlinear integer programming, neural networks, and Bayesian optimization. Inorder to assess how well these new approaches behave on a set of commonchallenges often faced in real-world data, we hosted a competition at the 2022Genetic and Evolutionary Computation Conference consisting of differentsynthetic and real-world datasets which were blind to entrants. For thereal-world track, we assessed interpretability in a realistic way by using adomain expert to judge the trustworthiness of candidate models.We present anin-depth analysis of the results obtained in this competition, discuss currentchallenges of symbolic regression algorithms and highlight possibleimprovements for future competitions.

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition