  We present ECToNAS, a cost-efficient evolutionary cross-topology neural
architecture search algorithm that does not require any pre-trained meta
controllers. Our framework is able to select suitable network architectures for
different tasks and hyperparameter settings, independently performing
cross-topology optimisation where required. It is a hybrid approach that fuses
training and topology optimisation together into one lightweight,
resource-friendly process. We demonstrate the validity and power of this
approach with six standard data sets (CIFAR-10, CIFAR-100, EuroSAT, Fashion
MNIST, MNIST, SVHN), showcasing the algorithm's ability to not only optimise
the topology within an architectural type, but also to dynamically add and
remove convolutional cells when and where required, thus crossing boundaries
between different network types. This enables researchers without a background
in machine learning to make use of appropriate model types and topologies and
to apply machine learning methods in their domains, with a computationally
cheap, easy-to-use cross-topology neural architecture search framework that
fully encapsulates the topology optimisation within the training process.
