  We propose a new scalable method to optimize the architecture of an
artificial neural network. The proposed algorithm, called Greedy Search for
Neural Network Architecture, aims to determine a neural network with minimal
number of layers that is at least as performant as neural networks of the same
structure identified by other hyperparameter search algorithms in terms of
accuracy and computational cost. Numerical results performed on benchmark
datasets show that, for these datasets, our method outperforms state-of-the-art
hyperparameter optimization algorithms in terms of attainable predictive
performance by the selected neural network architecture, and time-to-solution
for the hyperparameter optimization to complete.
