HBO-NAS: class-aware zero-cost fitness for diversity-preserving neural architecture search through hybrid breeding...

Neural Architecture Search (NAS) with evolutionary computing increasingly relies on zero-cost proxies to mitigate the prohibitive computational cost of training candidate networks. However, existing proxies are mainly designed to maximize score-accuracy correlation, neglecting the landscape structure of objectives required to sustain population diversity. To address this issue, a class-aware, training-free objective function is proposed, which utilizes intra-class compactness and inter-class separability to induce a structured framework, a multi-modal fitness landscape that naturally prevents premature convergence. This capability effectively facilitates the discovery of a broader range of different, high-performing structures. When evaluated using Hybrid Breeding Optimization algorithm, our method consistently yields superior optimization performance, achieving the average accuracy of 71.18% on ImageNet16-120 within the DARTS search space, which is nearly equal to the reported best-performing architecture with the accuracy of 72.00%, while maintaining a high level of population diversity. These findings show the critical shift towards a search-centric perspective, where shaping the landscape structure of objectives is as important as ranking fidelity for discovering diverse structures.