Abstract
Neural Architecture Search (NAS) automates the design of deep neural networks but remains computationally expensive, particularly in multi-objective settings. Existing predictor-assisted evolutionary NAS methods suffer from slow convergence and rank disorder, which undermines prediction accuracy. To overcome these limitations, we propose CHENAS: a Classifier-assisted multi-objective Hybrid Evolutionary NAS framework. CHENAS combines the global exploration of evolutionary algorithms with the local refinement of gradient-based optimization to accelerate convergence and enhance solution quality. A novel dominance classifier predicts Pareto dominance relationships among candidate architectures, reframing multi-objective optimization as a classification task and mitigating rank disorder. To further improve efficiency, we employ a contrastive learning-based autoencoder that maps architectures into a continuous, structured latent space tailored for dominance prediction. Experiments on several benchmark datasets demonstrate that CHENAS outperforms state-of-the-art NAS approaches in identifying high-performing architectures across multiple objectives. Future work will focus on improving the computational efficiency of the framework and extending it to other application domains.