Back to the Future - Revisiting OrdinalGP and Trustable Models After a Decade. - Mark E. Kotanchek; Nathan Haut (GPTP, 2021)

Active learning improves performance on symbolic regression tasks in StackGP. - Nathan Haut; Wolfgang Banzhaf; Bill Punch 001 (GECCO Companion, 2022)

Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks. - Nathan Haut; Wolfgang Banzhaf; Bill Punch 001 (GPTP, 2022)

Active Learning Improves Performance on Symbolic RegressionTasks in StackGP. - Nathan Haut; Wolfgang Banzhaf; Bill Punch 001 (CoRR, 2022)

Active Learning Informs Symbolic Regression Model Development in Genetic Programming. - Nathan Haut; Bill Punch 001; Wolfgang Banzhaf (GECCO Companion, 2023)

Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming. - Nathan Haut; Wolfgang Banzhaf; Bill Punch 001; Dirk Colbry (GPTP, 2023)

Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data. - Yuri Lavinas; Nathan Haut; William Punch 001; Wolfgang Banzhaf; Sylvain Cussat-Blanc (CEC, 2024)

Dynamically Sampling biomedical Images For Genetic Programming. - Yuri Lavinas; Nathaniel Haut; William Punch 001; Wolfgang Banzhaf; Sylvain Cussat-Blanc (GECCO Companion, 2024)

Adaptive Sampling of Biomedical Images with Cartesian Genetic Programming. - Yuri Lavinas; Nathan Haut; William Punch 001; Wolfgang Banzhaf; Sylvain Cussat-Blanc (PPSN (1), 2024)

Sharpness-Aware Minimization in Genetic Programming. - Illya Bakurov; Nathan Haut; Wolfgang Banzhaf (CoRR, 2024)

Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models. - Nathan Haut; Zenas Huang; Adam Alessio (CoRR, 2025)

Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression. - Nathan Haut; Wolfgang Banzhaf; Bill Punch 001 (IEEE Trans. Evol. Comput., 2025)