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A Survey on Evolutionary Neural Architecture Search

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arxiv 2008.10937 v4 pith:U5GX3ZGG submitted 2020-08-25 cs.NE

Yuqiao Liu , Yanan Sun , Bing Xue , Mengjie Zhang , Gary G. Yen , Kay Chen Tan This is my paper · ORCID
classification cs.NE
keywords designmethodsneuralarchitecturearchitecturesdnnsec-basedevolutionary
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Deep Neural Networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed with rich expertise. However, such a design process is labour intensive because of the trial-and-error process, and also not easy to realize due to the rare expertise in practice. Neural Architecture Search (NAS) is a type of technology that can design the architectures automatically. Among different methods to realize NAS, Evolutionary Computation (EC) methods have recently gained much attention and success. Unfortunately, there has not yet been a comprehensive summary of the EC-based NAS algorithms. This paper reviews over 200 papers of most recent EC-based NAS methods in light of the core components, to systematically discuss their design principles as well as justifications on the design. Furthermore, current challenges and issues are also discussed to identify future research in this emerging field.

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Cited by 3 Pith papers

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