  Neuro-Evolution is a field of study that has recently gained significantly
increased traction in the deep learning community. It combines deep neural
networks and evolutionary algorithms to improve and/or automate the
construction of neural networks. Recent Neuro-Evolution approaches have shown
promising results, rivaling hand-crafted neural networks in terms of accuracy.
A two-step approach is introduced where a convolutional autoencoder is created
that efficiently compresses the input data in the first step, and a
convolutional neural network is created to classify the compressed data in the
second step. The creation of networks in both steps is guided by by an
evolutionary process, where new networks are constantly being generated by
mutating members of a collection of existing networks. Additionally, a method
is introduced that considers the trade-off between compression and information
loss of different convolutional autoencoders. This is used to select the
optimal convolutional autoencoder from among those evolved to compress the data
for the second step. The complete framework is implemented, tested on the
popular CIFAR-10 data set, and the results are discussed. Finally, a number of
possible directions for future work with this particular framework in mind are
considered, including opportunities to improve its efficiency and its
application in particular areas.
