GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

• URL: http://arxiv.org/abs/2603.12292v1
• Date: Tue, 10 Mar 2026 20:13:39 GMT
• Title: GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
• Abstract summary: We report on the benchmarking of Beagle on the Feynman Symbolic Regression dataset.<n>Results demonstrate that the Beagle's GPU-aided Symbolic Regression significantly outperforms leading CPU-based frameworks.
• Score: 0.9744346550335888
• License: http://creativecommons.org/licenses/by/4.0/
• 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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