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Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers

Cardoso, Rui P; Hart, Emma; Burth Kurka, David; Pitt, Jeremy


Rui P Cardoso

David Burth Kurka

Jeremy Pitt


Using 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 computationally prohibitive. Here we propose a method to overcome this limitation by using a surrogate model which estimates the behavioural distance between two neural network architectures required to calculate the sparseness term in Novelty Search. We demonstrate a speedup of 10 times over previous work and significantly improve on previous reported results on three benchmark datasets from Computer Vision-CIFAR-10, CIFAR-100, and SVHN. This results from the expanded architecture search space facilitated by using a surrogate. Our method represents an improved paradigm for implementing horizontal scaling of learning algorithms by making an explicit search for diversity considerably more tractable for the same bounded resources.

Presentation Conference Type Conference Paper (Published)
Conference Name EvoSTAR
Start Date Apr 20, 2022
End Date Apr 22, 2022
Acceptance Date Jan 26, 2022
Online Publication Date Apr 15, 2022
Publication Date 2022
Deposit Date Feb 7, 2022
Publicly Available Date Apr 16, 2023
Publisher Springer
Pages 418-434
Series Title Lecture Notes in Computer Science
Series Number 13224
Book Title Applications of Evolutionary Computation: EvoApplications 2022
ISBN 978-3-031-02461-0
Keywords diversity, ensemble, novelty search, surrogate
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Augmenting Novelty Search With A Surrogate Model To Engineer Meta-Diversity In Ensembles Of Classifiers (677 Kb)

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