HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization
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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.
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