  Machine learning models work better when curated features are provided to
them. Feature engineering methods have been usually used as a preprocessing
step to obtain or build a proper feature set. In late years, autoencoders (a
specific type of symmetrical neural network) have been widely used to perform
representation learning, proving their competitiveness against classical
feature engineering algorithms. The main obstacle in the use of autoencoders is
finding a good architecture, a process that most experts confront manually. An
automated autoencoder architecture search procedure, based on evolutionary
methods, is proposed in this paper. The methodology is tested against nine
heterogeneous data sets. The obtained results show the ability of this approach
to find better architectures, able to concentrate most of the useful
information in a minimized coding, in a reduced time.
