  Neural Architecture Search (NAS) aims to optimize deep neural networks'
architecture for better accuracy or smaller computational cost and has recently
gained more research interests. Despite various successful approaches proposed
to solve the NAS task, the landscape of it, along with its properties, are
rarely investigated. In this paper, we argue for the necessity of studying the
landscape property thereof and propose to use the so-called Exploratory
Landscape Analysis (ELA) techniques for this goal. Taking a broad set of
designs of the deep convolutional network, we conduct extensive experimentation
to obtain their performance. Based on our analysis of the experimental results,
we observed high similarities between well-performing architecture designs,
which is then used to significantly narrow the search space to improve the
efficiency of any NAS algorithm. Moreover, we extract the ELA features over the
NAS landscapes on three common image classification data sets, MNIST, Fashion,
and CIFAR-10, which shows that the NAS landscape can be distinguished for those
three data sets. Also, when comparing to the ELA features of the well-known
Black-Box Optimization Benchmarking (BBOB) problem set, we found out that the
NAS landscapes surprisingly form a new problem class on its own, which can be
separated from all $24$ BBOB problems. Given this interesting observation, we,
therefore, state the importance of further investigation on selecting an
efficient optimizer for the NAS landscape as well as the necessity of
augmenting the current benchmark problem set.
