Surrogate-Assisted Neural Architecture Search With Performance Clustering

Evolutionary neural architecture search (ENAS) automates the design of neural network architectures but incurs significant computational costs during the evaluation phase of candidate neural networks in the evolutionary algorithm. To address this issue, surrogate models are widely adopted to estimate the performance of candidate architectures, thereby reducing resource consumption. However, existing surrogate models are primarily based on regression or classification. They are often hindered by interference from low-quality architectures, which limits search efficiency and final performance. To overcome these challenges, we propose surrogate-assisted neural architecture search with performance clustering (PCNAS). PCNAS introduces a contrastive learning-based performance clustering encoder (PCE). The PCE maps candidate architectures into an embedding space to form high-performance clusters while maximizing separation from low-performance architectures. This design mitigates the negative influence of poor-quality candidates. To enhance the discriminative power of the PCE, we incorporate a staged sample generation strategy to strengthen the hierarchical representation capacity of the PCE. Additionally, we introduce a training-free simple classifier to efficiently select high-performance architectures from the embedding space. PCNAS achieves high accuracy with low computational cost on NAS-Bench-101 and NAS-Bench- 201, outperforming previous approaches. Furthermore, PCNAS exhibits strong efficiency in the DARTS search space, discovering a high-performing architecture with a 2.53% error rate using only 0.5 GPU days of search cost, and on MobileNetV3 with ImageNet, achieving 78.12% top-1 accuracy using only 0.3 GPU days.