5 citations · 6 across the 10 of their papers we have counts for
10 papers
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…
EvoIQA - Explaining Image Distortions with Evolved White-Box Logic
Ruchika Gupta, Illya Bakurov, Nathan Haut +1
Traditional Image Quality Assessment (IQA) metrics typically fall into one of two extremes: rigid, hand-crafted mathematical models or "black-box" deep learning architectures that…
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…
Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models
Nathan Haut, Zenas Huang, Adam Alessio
Choosing models from a well-fitted evolved population that generalizes beyond training data is difficult. We introduce a pragmatic method to estimate model complexity using Hessian…
Genetic Programming for the Nuclear Many-Body Problem: a Guide
Illya Bakurov, Pablo Giuliani, Kyle Godbey +3
Genetic Programming is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use…
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…
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP
Nathan Haut, Wolfgang Banzhaf, Bill Punch
In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve…