  Deep Neural Networks (DNN's) are a widely-used solution for a variety of
machine learning problems. However, it is often necessary to invest a
significant amount of a data scientist's time to pre-process input data, test
different neural network architectures, and tune hyper-parameters for optimal
performance. Automated machine learning (autoML) methods automatically search
the architecture and hyper-parameter space for optimal neural networks.
However, current state-of-the-art (SOTA) methods do not include traditional
methods for manipulating input data as part of the algorithmic search space. We
adapt the Evolutionary Multi-objective Algorithm Design Engine (EMADE), a
multi-objective evolutionary search framework for traditional machine learning
methods, to perform neural architecture search. We also integrate EMADE's
signal processing and image processing primitives. These primitives allow EMADE
to manipulate input data before ingestion into the simultaneously evolved DNN.
We show that including these methods as part of the search space shows
potential to provide benefits to performance on the CIFAR-10 image
classification benchmark dataset.
