Efficient evolutionary neural architecture search based on hybrid search space

Manually designed convolutional neural networks have demonstrated excellent performance in various domains, but designing neural networks suitable for specific tasks poses significant challenges, and the emergence of Neural Structure Search (NAS) provides a new solution to this problem. However, existing algorithms either focus solely on network lightweight, resulting in subpar network performance, or excessively emphasize performance, leading to substantial network redundancy. With consideration for both network parameters and performance, this paper designs a hybrid search space based on residual modules and RepVGG modules using genetic algorithm, and stacks them together to form a more efficient network. To achieve this, we propose an efficient variable-length encoding strategy, utilizing units as the fundamental encoding space to encode variable-length individuals; we design evolutionary operations encompassing single-point crossover and three types of mutation operators to ensure population diversity; during training, a random forest-based performance predictor is employed to significantly shorten the network search time. To demonstrate the effectiveness of the proposed algorithm, we introduce the concept of transfer learning, which involves decoding the globally optimal solution, fine-tuning it, and then transferring it to three categories of real-world application datasets. Through comparisons with various algorithms, our approach consistently achieved leading performance.