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The performance of a deep neural network is heavily dependent on itsarchitecture and various neural architecture search strategies have beendeveloped for automated network architecture design. Recently, evolutionaryneural architecture search (ENAS) has received increasing attention due to theattractive global optimization capability of evolutionary algorithms. However,ENAS suffers from extremely high computation costs because a large number ofperformance evaluations is usually required in evolutionary optimization andtraining deep neural networks is itself computationally very intensive. Toaddress this issue, this paper proposes a new evolutionary framework for fastENAS based on directed acyclic graph, in which parents are randomly sampled andtrained on each mini-batch of training data. In addition, a node inheritancestrategy is adopted to generate offspring individuals and their fitness isdirectly evaluated without training. To enhance the feature processingcapability of the evolved neural networks, we also encode a channel attentionmechanism in the search space. We evaluate the proposed algorithm on the widelyused datasets, in comparison with 26 state-of-the-art peer algorithms. Ourexperimental results show the proposed algorithm is not only computationallymuch more efficiently, but also highly competitive in learning performance.

Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search