1. Aldeia, G.S.I., de França, F.O.: Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set. Genet. Program Evolvable Mach. 23(3), 309–349 (2022)
2. Arnaldo, I., Krawiec, K., O’Reilly, U.M.: Multiple regression genetic programming. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 879–886 (2014)
3. Biggio, L., Bendinelli, T., Lucchi, A., Parascandolo, G.: A seq2seq approach to symbolic regression. In: Learning Meets Combinatorial Algorithms at NeurIPS2020 (2020)
4. Biggio, L., Bendinelli, T., Neitz, A., Lucchi, A., Parascandolo, G.: Neural symbolic regression that scales. In: International Conference on Machine Learning, pp. 936–945. PMLR (2021)
5. Bouthillier, X., et al.: Accounting for variance in machine learning benchmarks. CoRR abs/2103.03098 (2021). https://arxiv.org/abs/2103.03098