What Is Neural Architecture Search (NAS)?

Designing neural network architectures traditionally required extensive expertise and experimentation. NAS automates this by defining a search space of possible architectures (layer types, connections, hyperparameters) and using optimization algorithms — including reinforcement learning, evolutionary algorithms, or gradient-based methods — to find architectures that perform best on a target task. NAS discovered architectures like EfficientNet and NASNet that outperformed human-designed networks. However, early NAS was extremely expensive (thousands of GPU hours). Modern efficient NAS methods like DARTS reduce this cost dramatically. NAS has been influential in both computer vision and NLP, and Google used NAS to help design components of their production models.