  Evolution-based neural architecture search requires high computational
resources, resulting in long search time. In this work, we propose a framework
of applying the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to the
neural architecture search problem called CMANAS, which achieves better results
than previous evolution-based methods while reducing the search time
significantly. The architectures are modelled using a normal distribution,
which is updated using CMA-ES based on the fitness of the sampled population.
We used the accuracy of a trained one shot model (OSM) on the validation data
as a prediction of the fitness of an individual architecture to reduce the
search time. We also used an architecture-fitness table (AF table) for keeping
record of the already evaluated architecture, thus further reducing the search
time. CMANAS finished the architecture search on CIFAR-10 with the top-1 test
accuracy of 97.44% in 0.45 GPU day and on CIFAR-100 with the top-1 test
accuracy of 83.24% for 0.6 GPU day on a single GPU. The top architectures from
the searches on CIFAR-10 and CIFAR-100 were then transferred to ImageNet,
achieving the top-5 accuracy of 92.6% and 92.1%, respectively.
