- author = "Guilherme S. {Imai Aldeia} and Hengzhe Zhang and Geoffrey Bomarito and Miles Cranmer and Alcides Fonseca and Bogdan Burlacu and William G. {La Cava} and Fabricio {Olivetti de Franca}",
- title = "Call for Action: towards the next generation of symbolic regression benchmark",
- booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
- year = "2025",
- editor = "Gabriel Kronberger and Fabricio {Olivetti de Franca} and William {La Cava} and Steven Gustafson",
- pages = "2529--2538",
- address = "Malaga, Spain",
- series = "GECCO '25 Companion",
- arxiv_month = "6 May",
- month = "14-18 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Machine Learning, cs.LG, Neural and Evolutionary Computing, cs.NE, symbolic regression, benchmark, srbench",
- isbn13 = "979-8-4007-1464-1",
-
URL = "
https://doi.org/10.48550/arXiv.2505.03977",
-
URL = "https://doi.org/10.1145/3712255.3734309",
-
DOI = "
10.1145/3712255.3734309",
-
code_url = "https://cavalab.org/srbench/",
- size = "10 pages",
- abstract = "Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. we present an updated version of SRBench. Our benchmark expands the previous one by nearly doubling the number of evaluated methods, refining evaluation metrics, and using improved visualizations of the results to understand the performances. Additionally, we analyze trade-offs between model complexity, accuracy, and energy consumption. Our results show that no single algorithm dominates across all datasets. We propose a call for action from SR community in maintaining and evolving SRBench as a living benchmark that reflects the state-of-the-art in symbolic regression, by standardizing hyperparameter tuning, execution constraints, and computational resource allocation. We also propose deprecation criteria to maintain the benchmark's relevance and discuss best practices for improving SR algorithms, such as adaptive hyperparameter tuning and energy-efficient implementations.",
-
notes = "Aug 2026 replace misc arXiv:2505.03977v1 with
2025:GECCOcomp in Genetic and Evolutionary Computation
Conference (GECCO 2025) Symbolic Regression Workshop
https://heal.heuristiclab.com/research/symreg_workshop/GECCO_2025
GECCO-2025 A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",
