Nathan Haut
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View article: Genetic programming for the nuclear many-body problem: a guide
Genetic programming for the nuclear many-body problem: a guide Open
Genetic Programming (GP) is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use GP to develop surrogate models to mitigate the computa…
View article: Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models
Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models Open
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 rank for post-processing selection. Complexity is approxim…
View article: Genetic Programming for the Nuclear Many-Body Problem: a Guide
Genetic Programming for the Nuclear Many-Body Problem: a Guide Open
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 Genetic Programming to develop surrogate models to mitigate…
View article: Sharpness-Aware Minimization in Genetic Programming
Sharpness-Aware Minimization in Genetic Programming Open
Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) function and the so-called fitness sharpness. The latter serves a…
View article: Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression Open
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 be exploited to select informative training data points by…
View article: Correlation versus RMSE Loss Functions in Symbolic Regression Tasks
Correlation versus RMSE Loss Functions in Symbolic Regression Tasks Open
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 with an alignment step to conclude the evolution led to sign…
View article: Active Learning Improves Performance on Symbolic RegressionTasks in StackGP
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP Open
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 the model the system incrementally adds a data point such t…
View article: Development and Evaluation of an ODE Representation of 3D Subsurface Tile Drainage Flow Using the HLM Flood Forecasting System
Development and Evaluation of an ODE Representation of 3D Subsurface Tile Drainage Flow Using the HLM Flood Forecasting System Open
We developed a new ordinary differential equation (ODE) to represent subsurface flows from a hillslope with artificial subsurface drainage (tiling) into an adjacent channel. Our ODE is based on a derived storage‐discharge relationship from…