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

This article 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...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 29; no. 4; pp. 1100 - 1111
Main Authors: Haut, Nathan, Banzhaf, Wolfgang, Punch, Bill
Format: Journal Article
Language:English
Published: IEEE 01.08.2025
Subjects:
Active learning (AL)
Computational modeling
Data models
Genetic programming
Labeling
Machine learning
Mathematical models
Measurement
symbolic regression
Training
Training data
Uncertainty
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:This article 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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2024.3471341