GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
An Introduction and Tutorial for the Beagle Framework
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 introduc…
The Effects of Population Size on the Performance of BEAGLE GPU-Based Genetic Programming Runs
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-e…
EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming
Tree-based Genetic Programming (TGP) is a key evolutionary algorithm widely used in symbolic regression, feature engineering, and scientific modeling. Its high computational demands make GPU acceleration essential for sc…
Feature EngineeringGPUregressionSymbolic RegressionSymbolic Regression via Neural-Guided Genetic Programming Population Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believed to be NP-hard. Prior approaches to so…
Combinatorial OptimizationregressionSymbolic RegressionSymbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believed to be NP-hard. Prior approaches to so…
Combinatorial OptimizationDeep Reinforcement Learningregressionreinforcement-learning+3