Semantic Scholar Open Access 2020 547 sitasi

A Survey on Evolutionary Neural Architecture Search

Yuqiao Liu Y. Sun Bing Xue Mengjie Zhang G. Yen

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Abstrak

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 labor-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, the 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 article reviews over 200 articles of most recent EC-based NAS methods in light of the core components, to systematically discuss their design principles and justifications on the design. Furthermore, current challenges and issues are also discussed to identify future research in this emerging field.

Topik & Kata Kunci

Computer Science Medicine

Penulis (5)

Yuqiao Liu

Y. Sun

Bing Xue

Mengjie Zhang

G. Yen

Format Sitasi

APA MLA BibTeX

Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G. (2020). A Survey on Evolutionary Neural Architecture Search. https://doi.org/10.1109/TNNLS.2021.3100554

Akses Cepat

Lihat di Sumber doi.org/10.1109/TNNLS.2021.3100554

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 547×
Sumber Database: Semantic Scholar
DOI: 10.1109/TNNLS.2021.3100554
Akses: Open Access ✓