  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.
