Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models.We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions.

翻译：符号回归旨在寻找能准确描述所研究现象的解析表达式。该方法的主要吸引力在于其能返回一个可解释的模型，从而为用户提供洞察。历史上，大多数符号回归算法基于进化算法。然而，近期涌现出一批新的方案，转而采用枚举算法、混合整数线性规划、神经网络和贝叶斯优化等方法。为评估这些新方法在处理真实世界数据中常见挑战时的表现，我们在2022年遗传与进化计算大会上举办了一场竞赛，包含多个对参赛者盲测的合成与真实世界数据集。针对真实世界赛道，我们通过邀请领域专家评判候选模型的可信度，以现实方式评估了模型的可解释性。本文对此次竞赛结果进行了深入分析，探讨了符号回归算法当前面临的挑战，并指出了未来竞赛可改进的方向。