GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914388283228160 |
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| author | Haut, Nathan Basin, Ilya Kianinejad, Marzieh Gupta, Ruchika Smith, Elijah Perrico, Zachary Banzhaf, Wolfgang |
| author_facet | Haut, Nathan Basin, Ilya Kianinejad, Marzieh Gupta, Ruchika Smith, Elijah Perrico, Zachary Banzhaf, Wolfgang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12292 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework Haut, Nathan Basin, Ilya Kianinejad, Marzieh Gupta, Ruchika Smith, Elijah Perrico, Zachary Banzhaf, Wolfgang Neural and Evolutionary Computing 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. |
| title | GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2603.12292 |