Abstract
Neural Architecture Search (NAS) aims to automatically find neural network architectures competitive with human-designed ones. Despite the remarkable progress achieved, existing NAS methods still suffer from vast computational resources cost. Inspired by MFEA, we model the NAS task as a two-factorial problem and propose a multifactorial evolutionary neural architecture search (MFENAS) algorithm to solve it. MFENAS divides a population into two subgroups according to factors, and then the factors influence the evolution and knowledge transfer between subgroups. Experimental results of NATS-Bench demonstrate the efficiency of the proposed MFENAS in finding optimal structures under resource constraints compared to other state-of-the-art methods.
