5 citations · 5 across the 3 of their papers we have counts for
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cs.NE2023
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…
cs.NE2022★ 5 cited
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…