5 citations · 6 across the 10 of their papers we have counts for
6 papers · 1 filter
An Introduction and Tutorial for the Beagle Framework
Ilya Basin, Nathan Haut
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. T…
The Effects of Population Size on the Performance of BEAGLE GPU-Based Genetic Programming Runs
Nathan Haut, Ilya Basin, Ruchika Gupta +4
The Beagle framework, through GPU-based Genetic Programming, enables population dynamics previously unattainable (within practical time frames) by CPU-constrained Genetic Programmi…
GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
Nathan Haut, Ilya Basin, Marzieh Kianinejad +4
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 popu…
Sharpness-Aware Minimization in Genetic Programming
Illya Bakurov, Nathan Haut, Wolfgang Banzhaf
Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) functi…
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
Nathan Haut, Wolfgang Banzhaf, Bill Punch
This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can…
Correlation versus RMSE Loss Functions in Symbolic Regression Tasks
Nathan Haut, Wolfgang Banzhaf, Bill Punch
The use of correlation as a fitness function is explored in symbolic regression tasks and the performance is compared against the typical RMSE fitness function. Using correlation w…