Improving Novelty Search with a Surrogate Model and Accuracy Objectives to Build High-Performing Ensembles of Classifiers
(2025)
Journal Article
Cardoso, R. P., Hart, E., & Pitt, J. V. (2025). Improving Novelty Search with a Surrogate Model and Accuracy Objectives to Build High-Performing Ensembles of Classifiers. SN Computer Science, 6(6), Article 631. https://doi.org/10.1007/s42979-025-04056-4
Neuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification. However, using gradient descent to train evolved architectures during the search can be computationa... Read More about Improving Novelty Search with a Surrogate Model and Accuracy Objectives to Build High-Performing Ensembles of Classifiers.