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Found 3 papers, 1 papers with code
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming.
Correlation versus RMSE Loss Functions in Symbolic Regression Tasks
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.
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP
The approach begins with a small number of data points for StackGP to model.
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