论文标题
Hivenas：使用人造蜜蜂菌落优化的神经建筑搜索
HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization
论文作者
论文摘要
传统的神经网络发展过程需要大量的专家知识，并在很大程度上依赖直觉和反复试验。引入了神经体系结构搜索（NAS）框架，以便于稳定地搜索网络拓扑，并促进了神经网络的自动开发。尽管某些优化方法（例如遗传算法）已在NAS环境中进行了广泛的探索，但尚未研究其他元启发式优化算法。在这项研究中，我们评估了用于神经结构搜索的人造蜜蜂菌落优化的可行性。我们提出的框架hivenas在一小部分时间内优于现有的基于智能的NAS框架。
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 investigated. In this study, we evaluate the viability of Artificial Bee Colony optimization for Neural Architecture Search. Our proposed framework, HiveNAS, outperforms existing state-of-the-art Swarm Intelligence-based NAS frameworks in a fraction of the time.
