GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

TL;DR

This paper introduces Beagle, a GPU-accelerated framework for symbolic regression using genetic programming, demonstrating significant performance improvements over CPU-based systems through benchmarking on the Feynman dataset.

Contribution

The paper presents Beagle, a novel GPU-based framework for genetic programming in symbolic regression, with optimized processing for high throughput and benchmarking results showing superior performance.

Findings

Beagle outperforms CPU-based frameworks in symbolic regression tasks.

GPU acceleration significantly increases throughput for genetic programming.

Different fitness functions impact the performance and results of the regression.

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.