关键词: 进化算法, 神经网络架构搜索, 混合启发信息, 自适应协方差策略, 蒙特卡洛交换采样

Abstract: To address the challenges of Neural network Architecture Search (NAS), a NAS Guided by Hybrid Heuristic Information (GHHI-NAS) algorithm was proposed. First, a heuristic information construction module integrating prior knowledge with local search feedback was designed to generate multidimensional dynamic heuristic indicators, and a hybrid update strategy was combined to guide architecture search, effectively mitigating the global exploration deficiency and local optima traps caused by unidirectional updates in traditional NAS. Second, an adaptive Covariance Matrix Evolution Strategy (CMA-ES) framework was adopted, enhanced by a hybrid fitness evaluation function to escape small-model traps during early iterations. Finally, a fitness-sharing strategy was implemented to smooth evaluation noise and improve population diversity. Additionally, a penalty-based Monte Carlo exchange sampling method was introduced to reduce performance degradation caused by random sampling. Experimental results demonstrated that GHHI-NAS achieved validation accuracies of 97.55% and 83.44% on CIFAR-10 and CIFAR-100, respectively, along with a test error rate of 24.7% on ImageNet. It also exhibited outstanding performance on NAS-Bench-201, matching or slightly surpassing state-of-the-art Evolutionary NAS (ENAS) algorithms while requiring only 0.12 GPU-Days, thereby achieving low search costs and high-performance architecture discovery.

Key words: Evolutionary Algorithm (EA), Neural network Architecture Search (NAS), hybrid heuristic information, adaptive Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Monte Carlo swap sampling