GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
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.
Cite
@article{arxiv.2603.12292,
title = {GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework},
author = {Nathan Haut and Ilya Basin and Marzieh Kianinejad and Ruchika Gupta and Elijah Smith and Zachary Perrico and Wolfgang Banzhaf},
journal= {arXiv preprint arXiv:2603.12292},
year = {2026}
}