AS-NAS: Adaptive Scalable Neural Architecture Search With Reinforced Evolutionary Algorithm for Deep Learning

Neural architecture search (NAS) is a challenging problem in the design of deep learning due to its nonconvexity. To address this problem, an adaptive scalable NAS method (AS-NAS) is proposed based on the reinforced I-Ching divination evolutionary algorithm (IDEA) and variable-architecture encoding...

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Veröffentlicht in:	IEEE transactions on evolutionary computation 2021-10, Vol.25 (5), p.830-841
Hauptverfasser:	Zhang, Tong, Lei, Chunyu, Zhang, Zongyan, Meng, Xian-Bing, Chen, C. L. Philip
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive search techniques Aerospace electronics Computational modeling Computer architecture Deep learning Encoding Evolutionary algorithms Genetic algorithms I-Ching divination evolutionary algorithm (IDEA) Machine learning Microprocessors neural architecture search (NAS) Regularization reinforced operator controller Search problems Searching Task analysis variable-architecture encoding
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