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

TL;DR

This paper investigates active learning strategies in genetic programming for symbolic regression, focusing on uncertainty and diversity metrics, and proposes a Pareto optimization method to select informative data points effectively.

Contribution

It introduces a combined uncertainty and diversity approach using Pareto optimization to improve data selection in genetic programming.

Findings

Differential entropy outperforms other uncertainty metrics.

Correlation as a diversity metric is more effective than Euclidean distance.

Pareto optimization balances uncertainty and diversity for better data selection.

Abstract

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 be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric. We explored several uncertainty metrics and found that differential entropy performed the best. We also compared two data diversity metrics and found that correlation as a diversity metric performs better than minimum Euclidean distance, although there are some drawbacks that prevent correlation from being used on all problems. Finally, we combined uncertainty and diversity using a Pareto optimization approach to allow both to be considered in a balanced way to guide the selection of informative and unique data points for training.