  Deep Evolutionary Network Structured Representation (DENSER) is a novel
approach to automatically design Artificial Neural Networks (ANNs) using
Evolutionary Computation. The algorithm not only searches for the best network
topology (e.g., number of layers, type of layers), but also tunes
hyper-parameters, such as, learning parameters or data augmentation parameters.
The automatic design is achieved using a representation with two distinct
levels, where the outer level encodes the general structure of the network,
i.e., the sequence of layers, and the inner level encodes the parameters
associated with each layer. The allowed layers and range of the
hyper-parameters values are defined by means of a human-readable Context-Free
Grammar. DENSER was used to evolve ANNs for CIFAR-10, obtaining an average test
accuracy of 94.13%. The networks evolved for the CIFA--10 are tested on the
MNIST, Fashion-MNIST, and CIFAR-100; the results are highly competitive, and on
the CIFAR-100 we report a test accuracy of 78.75%. To the best of our
knowledge, our CIFAR-100 results are the highest performing models generated by
methods that aim at the automatic design of Convolutional Neural Networks
(CNNs), and are amongst the best for manually designed and fine-tuned CNNs.
