Nathaniel Haut

According to our database, Nathaniel Haut authored at least 18 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

The Effects of Population Size on the Performance of BEAGLE GPU-Based Genetic Programming Runs.

Nathan Haut, Ilya Basin, Ruchika Gupta, Marzieh Kianinejad, Zachary P. Neal, Elijah Smith, Wolfgang Banzhaf

CoRR, April, 2026

EvoIQA - Explaining Image Distortions with Evolved White-Box Logic.

Ruchika Gupta, Illya Bakurov, Nathan Haut, Wolfgang Banzhaf

CoRR, March, 2026

GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework.

Nathan Haut, Ilya Basin, Marzieh Kianinejad, Ruchika Gupta, Elijah Smith, Zachary P. Neal, Wolfgang Banzhaf

CoRR, March, 2026

Reducing Computational Overhead in Biomedical Image Segmentation via Active Learning and PCA-Based Diversity Filtering in CGP.

Yuri Lavinas, Nathaniel Haut, Sylvain Cussat-Blanc, Wolfgang Banzhaf

Proceedings of the Genetic Programming - 29th European Conference, 2026

2025

Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression.

Nathan Haut, Wolfgang Banzhaf, Bill Punch

IEEE Trans. Evol. Comput., August, 2025

Rewarding Model Smoothness and Simplicity via Alternating Objectives in Symbolic Regression.

Nathan Haut, Mark E. Kotanchek

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models.

Nathan Haut, Zenas Huang, Adam Alessio

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

A Symbolic Hessian-Based Approach for Assessing Model Complexity in Symbolic Regression.

Nathan Haut, Stuart W. Card, Mark E. Kotanchek

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

2024

Sharpness-Aware Minimization in Genetic Programming.

Illya Bakurov, Nathan Haut, Wolfgang Banzhaf

CoRR, 2024

Adaptive Sampling of Biomedical Images with Cartesian Genetic Programming.

Yuri Lavinas, Nathan Haut, William Punch, Wolfgang Banzhaf, Sylvain Cussat-Blanc

Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024

Dynamically Sampling biomedical Images For Genetic Programming.

Yuri Lavinas, Nathaniel Haut, William Punch, Wolfgang Banzhaf, Sylvain Cussat-Blanc

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data.

Yuri Lavinas, Nathan Haut, William Punch, Wolfgang Banzhaf, Sylvain Cussat-Blanc

Proceedings of the IEEE Congress on Evolutionary Computation, 2024

2023

Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming.

Nathan Haut, Wolfgang Banzhaf, Bill Punch, Dirk Colbry

Proceedings of the Genetic Programming Theory and Practice XX [GPTP 2023], 2023

Active Learning Informs Symbolic Regression Model Development in Genetic Programming.

Nathan Haut, Bill Punch, Wolfgang Banzhaf

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

2022

Active Learning Improves Performance on Symbolic RegressionTasks in StackGP.

Nathan Haut, Wolfgang Banzhaf, Bill Punch

CoRR, 2022

Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks.

Nathan Haut, Wolfgang Banzhaf, Bill Punch

Proceedings of the Genetic Programming Theory and Practice XIX [GPTP 2022], 2022

Active learning improves performance on symbolic regression tasks in StackGP.

Nathan Haut, Wolfgang Banzhaf, Bill Punch

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021

Back to the Future - Revisiting OrdinalGP and Trustable Models After a Decade.

Mark E. Kotanchek, Nathan Haut

Proceedings of the Genetic Programming Theory and Practice XVIII [GPTP 2021]., 2021