[2603.12292] GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

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Computer Science > Neural and Evolutionary Computing

arXiv:2603.12292 (cs)

[Submitted on 10 Mar 2026]

Title:GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

Authors:Nathan Haut, Ilya Basin, Marzieh Kianinejad, Ruchika Gupta, Elijah Smith, Zachary Perrico, Wolfgang Banzhaf<br>View a PDF of the paper titled GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework, by Nathan Haut and 6 other authors

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Abstract:Beagle is a new software framework that enables execution of Genetic Programming tasks on the GPU. Currently available for symbolic regression, it processes individuals of the population and fitness cases for training in a way that maximizes throughput on extant GPU platforms. In this contribution, we report on the benchmarking of Beagle on the Feynman Symbolic Regression dataset and compare its performance with a fast CPU system called StackGP and the widely available PySR system under the same wall clock budget. We also report on the use of two different fitness functions, one a point-to-point error function, the other a correlation fitness function. The results demonstrate that the Beagle's GPU-aided Symbolic Regression significantly outperforms leading CPU-based frameworks.

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Neural and Evolutionary Computing (cs.NE)

Cite as:<br>arXiv:2603.12292 [cs.NE]

(or<br>arXiv:2603.12292v1 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2603.12292

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arXiv-issued DOI via DataCite

Submission history<br>From: Nathaniel Haut [view email]<br>[v1]<br>Tue, 10 Mar 2026 20:13:39 UTC (1,535 KB)

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