  Deep Neural Networks (DNNs) have been successfully applied to a wide range of
problems. However, two main limitations are commonly pointed out. The first one
is that they require long time to design. The other is that they heavily rely
on labelled data, which can sometimes be costly and hard to obtain. In order to
address the first problem, neuroevolution has been proved to be a plausible
option to automate the design of DNNs. As for the second problem,
self-supervised learning has been used to leverage unlabelled data to learn
representations. Our goal is to study how neuroevolution can help
self-supervised learning to bridge the gap to supervised learning in terms of
performance. In this work, we propose a framework that is able to evolve deep
neural networks using self-supervised learning. Our results on the CIFAR-10
dataset show that it is possible to evolve adequate neural networks while
reducing the reliance on labelled data. Moreover, an analysis to the structure
of the evolved networks suggests that the amount of labelled data fed to them
has less effect on the structure of networks that learned via self-supervised
learning, when compared to individuals that relied on supervised learning.
