  In this paper, we present a novel neuroevolutionary method to identify the
architecture and hyperparameters of convolutional autoencoders. Remarkably, we
used a hypervolume indicator in the context of neural architecture search for
autoencoders, for the first time to our current knowledge. Results show that
images were compressed by a factor of more than 10, while still retaining
enough information to achieve image classification for the majority of the
tasks. Thus, this new approach can be used to speed up the AutoML pipeline for
image compression.
