cs.NE · 2607.06731 Copy arXiv ID · Jul 7, 2026 Save An Introduction and Tutorial for the Beagle Framework Authors: Ilya Basin , Nathan Haut
Organizations: Noblis, Reston VA, USA · Michigan State University, East Lansing MI 48824, USA
Abstract The Beagle framework is a GPU-based genetic programming framework that enables highly efficient genetic programming search using large population sizes by leveraging NVIDIA GPUs. This technical guide provides an introduction to the Beagle framework and provides detailed instructions for using the framework for symbolic regression problems.
Explore similar work Apr 27, 2026 · Nathan Haut, Ilya Basin, Ruchika Gupta +4 Genetic Algorithms Symbolic Regression
Sep 3, 2026 · Hao Mao, Xu Tony Liu, Shuai Lu +3 Symbolic Regression Genetic Algorithms
May 11, 2026 · Yanjie Li, Liping Zhang, Min Wu +6 Symbolic Regression Genetic Algorithms
Apr 27, 2026 · cs.NE J/K move · Enter open · S save
Nathan Haut, Ilya Basin, Ruchika Gupta, Marzieh Kianinejad +3
Michigan State University, East Lansing MI 48824, USA · Noblis, Reston VA , USA
The Beagle framework, through GPU-based Genetic Programming, enables population dynamics previously unattainable (within practical time frames) by CPU-constrained Genetic Programming systems. This work explores how GPU-enabled population sizes impact the success of training for symbolic regression problems. Specifically, when using constant population sizes, we see benefits of using very narrow and deep searches (as narrow as 1000 individuals) for some problems, while other problems benefit from very broad and shallow searches (as broad as 10 million individuals). We also explore stepped population sizes that start with large populations and drop to small populations to balance the breadth and depth of search.