Active Learning Informs Symbolic Regression Model Development in Genetic Programming

Active learning for genetic programming using model ensemble uncertainty was explored across a range of uncertainty metrics to determine if active learning can be used with GP to minimize training set sizes by selecting maximally informative samples to guide evolution. The choice of uncertainty metric was found to have a significant impact on the success of active learning to inform model development in genetic programming. Differential evolution was found to be an effective optimizer, likely due to the non-convex nature of the uncertainty space, while differential entropy was found to be an effective uncertainty metric. Uncertainty-based active learning was compared to two random sampling methods and the results show that active learning successfully identified informative samples and can be used with GP to reduce required training set sizes to arrive at a solution.