  Neuroevolutionary algorithms, automatic searches of neural network structures
by means of evolutionary techniques, are computationally costly procedures. In
spite of this, due to the great performance provided by the architectures which
are found, these methods are widely applied. The final outcome of
neuroevolutionary processes is the best structure found during the search, and
the rest of the procedure is commonly omitted in the literature. However, a
good amount of residual information consisting of valuable knowledge that can
be extracted is also produced during these searches. In this paper, we propose
an approach that extracts this information from neuroevolutionary runs, and use
it to build a metamodel that could positively impact future neural architecture
searches. More specifically, by inspecting the best structures found during
neuroevolutionary searches of generative adversarial networks with varying
characteristics (e.g., based on dense or convolutional layers), we propose a
Bayesian network-based model which can be used to either find strong neural
structures right away, conveniently initialize different structural searches
for different problems, or help future optimization of structures of any type
to keep finding increasingly better structures where uninformed methods get
stuck into local optima.
