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

• URL: http://arxiv.org/abs/2201.12896v1
• Date: Sun, 30 Jan 2022 19:13:32 GMT
• Title: Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers
• Authors: Rui P. Cardoso, Emma Hart, David Burth Kurka and Jeremy V. Pitt
• Abstract summary: Using Neuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification. We propose a method to overcome this limitation by using a surrogate model which estimates the behavioural distance between two neural network architectures.
• Score: 5.8497361730688695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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