HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization. (arXiv:2211.10250v1 [cs.NE])

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.

architecture artificial arxiv colony neural architecture search optimization search