  Neural architecture search (NAS), the study of automating the discovery of
optimal deep neural network architectures for tasks in domains such as computer
vision and natural language processing, has seen rapid growth in the machine
learning research community. While there have been many recent advancements in
NAS, there is still a significant focus on reducing the computational cost
incurred when validating discovered architectures by making search more
efficient. Evolutionary algorithms, specifically genetic algorithms, have a
history of usage in NAS and continue to gain popularity versus other
optimization approaches as a highly efficient way to explore the architecture
objective space. Most NAS research efforts have centered around computer vision
tasks and only recently have other modalities, such as the rapidly growing
field of natural language processing, been investigated in depth. In this work,
we show how genetic algorithms can be paired with lightly trained objective
predictors in an iterative cycle to accelerate multi-objective architectural
exploration in a way that works in the modalities of both machine translation
and image classification.
