  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 that the new point maximizes prediction uncertainty as measured by
the model ensemble. Symbolic regression is re-run with the larger data set.
This cycle continues until the system satisfies a termination criterion. We use
the Feynman AI benchmark set of equations to examine the ability of our method
to find appropriate models using fewer data points. The approach was found to
successfully rediscover 72 of the 100 Feynman equations using as few data
points as possible, and without use of domain expertise or data translation.
