GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
This work addresses the computational bottleneck in symbolic regression for researchers and practitioners by providing a GPU-accelerated solution, though it is incremental as it builds on existing genetic programming methods.
The authors tackled the problem of accelerating genetic programming for symbolic regression by developing the Beagle framework, which leverages GPU processing to achieve significantly higher performance compared to leading CPU-based systems like StackGP and PySR, as demonstrated on the Feynman dataset.
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.