Abstract
Evolutionary neural architecture search (ENAS) automates the design of high-performing neural networks but is often hindered by the high computational cost of evaluating individual architectures. Surrogate models mitigate this issue by predicting performance, yet their accuracy depends on the quality of training data and their ability to utilise insights from real evaluations. This paper presents homogeneous encoding-based ENAS (HENAS), a novel method addressing these challenges through two key innovations: homogeneous architecture augmentation and confidence-based prediction. Through homogeneous architecture augmentation, HENAS exploits redundant encodings in the MobileNetV3 search space to generate multiple representations of the same architecture, enhancing the surrogate model's training data without additional cost. Confidence-based prediction introduces a mechanism to identify architectures with uncertain performance estimates, prioritising them for evaluation. Integrated into an evolutionary framework, these techniques improve search efficiency and exploration. Experiments on CIFAR-10, CIFAR-100, and ImageNet show that HENAS achieves state-of-the-art performance with reduced computational expense. Ablation studies confirm the contributions of its core components, highlighting the value of redundancy exploitation and uncertainty management in surrogate-assisted ENAS.