Characterising the Double Descent of Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{dick:2024:GECCOcomp,

• author = "Grant Dick and Caitlin Owen",
• title = "Characterising the Double Descent of Symbolic Regression",
• booktitle = "Symbolic Regression",
• year = "2024",
• editor = "William {La Cava} and Steven Gustafson",
• pages = "2050--2057",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, symbolic regression, bias-variance tradeoff, double descent",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3664176",
• size = "8 pages",
• abstract = "Recent work has argued that many machine learning techniques exhibit a 'double descent' in model risk, where increasing model complexity beyond an interpolation zone can overcome the bias-variance tradeoff to produce large, over-parameterised models that generalise well to unseen data. While the double descent characteristic has been identified in many learning methods, it has not been explored within symbolic regression research. This paper presents an initial exploration into the presence of double descent behaviour in symbolic regression over a range of parameter settings. Results suggest that symbolic regression via genetic programming does not exhibit a clear double descent risk curve relative to model size or function set. Unlike other methods, models evolved through symbolic regression do not appear to strongly interpolate training data, which promotes a degree of robustness towards noise in training data. However, models evolved by symbolic regression can still be large and do not present a strong overfitting characteristic. Given that a prime motivation for symbolic regression is to produce compact interpretable models, these results suggest that methods aimed at regularising evolved models should be a key feature of all symbolic regression methods.",
• notes = "GECCO-2024 SymReg A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Grant Dick Caitlin A Owen

Citations