Technical survey of neural architecture search methods

This article examines automated approaches for discovering optimal neural network architectures without manual expert design. The survey discusses the evolution of Neural Architecture Search from early work in 2017 to contemporary methods, covering the three core components of NAS systems: search space definition, search strategy, and performance estimation. Rather than relying solely on sequential layer-wise operations, modern NAS approaches explore more sophisticated network topology representations. The article addresses how to ensure generated architectures remain valid while managing the complexity of architecture-specific parameters. Skip connections and attention mechanisms are explored as architectural components. The survey presents both efficient and comprehensive NAS methodologies to enable practitioners to find high-performance architectures more systematically than traditional manual design approaches.