Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming

By Ekaterina Vladislavleva, Guido Smits, Dick den Hertog. IEEE Transactions on Evolutionary Computation, 2009

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Contemporary Symbolic Regression Methods and their Relative Performance (2021, methodology): “…SR, including those based on syntactic complexity (i.e. related to the complexity of the symbolic model); those based on semantic complexity (i.e. related to the behavior of the model over the data) [23, 68]; those using both deﬁnitions [69]; and those estimating complexity via meta-learning [70].”
Interpretable scientific discovery with symbolic regression: a review (2022, neutral): “These include (1) physics-inspired equations [27, 60] and (2) real-valued symbolic equations [1, 14, 15, 16, 17, 18, 19, 61].”
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients (2019, background): “More complicated metrics have been proposed that capture hierarchical features of the tree and/or deduced properties of the resulting expression (Vladislavleva et al., 2008).”