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HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization

2022-11-18 14:11:47
Mohamed Shahawy, Elhadj Benkhelifa
arXiv_AI
         
Abstract

The traditional Neural Network-development process requires substantial expert knowledge and relies heavily on intuition and trial-and-error. Neural Architecture Search (NAS) frameworks were introduced to robustly search for network topologies, as well as facilitate the automated development of Neural Networks. While some optimization approaches -- such as Genetic Algorithms -- have been extensively explored in the NAS context, other Metaheuristic Optimization algorithms have not yet been evaluated. In this paper, we propose HiveNAS, the first Artificial Bee Colony-based NAS framework.

Abstract (translated)

URL

https://arxiv.org/abs/2211.10250

PDF

https://arxiv.org/pdf/2211.10250.pdf

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