GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
🤖 AI Summary
This work proposes Beagle, the first fully GPU-accelerated genetic programming framework tailored for symbolic regression. Addressing the computational inefficiency and poor scalability of traditional CPU-based approaches, Beagle leverages GPU parallelism to accelerate both population evolution and fitness evaluation across training samples, supporting diverse fitness functions such as pointwise error and correlation-based metrics. Experimental results on the Feynman dataset demonstrate that, under identical time budgets, Beagle substantially outperforms established CPU-based frameworks—including StackGP and PySR—achieving significantly higher throughput and superior solution quality in symbolic regression tasks.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Genetic Programming
Symbolic Regression
GPU Acceleration
Beagle Framework
Fitness Function
Innovation
Methods, ideas, or system contributions that make the work stand out.
GPU acceleration
Genetic Programming
Symbolic Regression
Beagle framework
Parallel computing
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