  This paper introduces NSGA-Net -- an evolutionary approach for neural
architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a
procedure considering multiple and conflicting objectives, (2) an efficient
procedure balancing exploration and exploitation of the space of potential
neural network architectures, and (3) a procedure finding a diverse set of
trade-off network architectures achieved in a single run. NSGA-Net is a
population-based search algorithm that explores a space of potential neural
network architectures in three steps, namely, a population initialization step
that is based on prior-knowledge from hand-crafted architectures, an
exploration step comprising crossover and mutation of architectures, and
finally an exploitation step that utilizes the hidden useful knowledge stored
in the entire history of evaluated neural architectures in the form of a
Bayesian Network. Experimental results suggest that combining the dual
objectives of minimizing an error metric and computational complexity, as
measured by FLOPs, allows NSGA-Net to find competitive neural architectures.
Moreover, NSGA-Net achieves error rate on the CIFAR-10 dataset on par with
other state-of-the-art NAS methods while using orders of magnitude less
computational resources. These results are encouraging and shows the promise to
further use of EC methods in various deep-learning paradigms.
