This chapter introduces Competition Neural Architecture Search (CNAS), a method for automatically designing neural network architectures. CNAS separates the search process into two parts: topological structure enumeration and calculation operation optimization. The topological structures are enumerated under depth and width constraints, while the calculation operations are optimized using derivative-free optimization (DFO) methods. A competition mechanism is employed to iteratively eliminate poorly performing structures, ensuring that the best architecture is selected. To improve efficiency, CNAS uses block-based search and experience reuse, leveraging historical data to warm-start the optimization process and simulate competitions. The chapter presents empirical results on image classification and denoising tasks, demonstrating that CNAS achieves competitive performance compared to manual designs and state-of-the-art NAS methods. The experiments highlight CNAS’s ability to efficiently explore the architecture space and produce high-quality network designs.