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This paper examines various methods of computing uncertainty and diversityfor active learning in genetic programming. We found that the model populationin genetic programming can be exploited to select informative training datapoints by using a model ensemble combined with an uncertainty metric. Weexplored several uncertainty metrics and found that differential entropyperformed the best. We also compared two data diversity metrics and found thatcorrelation as a diversity metric performs better than minimum Euclideandistance, although there are some drawbacks that prevent correlation from beingused on all problems. Finally, we combined uncertainty and diversity using aPareto optimization approach to allow both to be considered in a balanced wayto guide the selection of informative and unique data points for training.

Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression