Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models

Created by W.Langdon from gp-bibliography.bib Revision:1.8576

@InProceedings{haut:2025:GECCOcomp,

• author = "Nathan Haut and Zenas Huang and Adam Alessio",
• title = "Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "611--614",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, evolutionary algorithms, symbolic regression, intrinsic dimension, model selection, manifold learning: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726632",
• DOI = "doi:10.1145/3712255.3726632",
• size = "4 pages",
• abstract = "Selecting models from a well-fit, evolved population that generalizes beyond training data is difficult. We introduce a pragmatic method using Hessian rank to align model complexity with dataset intrinsic dimensionality (ID) for post-processing selection in symbolic regression. Our method efficiently estimates model complexity by averaging Hessian rank across strategic points, aligns this with dataset ID estimated using conventional ID estimators, and identifies an ideal complexity window that balances expressiveness with accuracy. Using StackGP with the Penn Machine Learning Benchmark, we demonstrate that models within this targeted complexity window generalize better than those outside it, without the bias common in methods reliant on user-defined parameters.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Nathaniel Haut Zenas Huang Adam Alessio

Citations