  Evolutionary algorithms (EA) based neural architecture search (NAS) involves
evaluating each architecture by training it from scratch, which is extremely
time-consuming. This can be reduced by using a supernet for estimating the
fitness of an architecture due to weight sharing among all architectures in the
search space. However, the estimated fitness is very noisy due to the
co-adaptation of the operations in the supernet which results in NAS methods
getting trapped in local optimum. In this paper, we propose a method called
NEvoNAS wherein the NAS problem is posed as a multi-objective problem with 2
objectives: (i) maximize architecture novelty, (ii) maximize architecture
fitness/accuracy. The novelty search is used for maintaining a diverse set of
solutions at each generation which helps avoiding local optimum traps while the
architecture fitness is calculated using supernet. NSGA-II is used for finding
the \textit{pareto optimal front} for the NAS problem and the best architecture
in the pareto front is returned as the searched architecture. Exerimentally,
NEvoNAS gives better results on 2 different search spaces while using
significantly less computational resources as compared to previous EA-based
methods. The code for our paper can be found in
https://github.com/nightstorm0909/NEvoNAS.
