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Proceedings of the Genetic and Evolutionary Computation Conference Companion
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 thisdoi:10.1145/3520304.3528889 fatcat:ipuvb4x5nbawxbijdstfxb64jq