An Enhanced Variant of Graph-Constrained Neural Multi-Objective Evolutionary Algorithm for Sparse Neural Network Training

Abstract

Sparse neural networks are essential for deploying deep learning models on resource-limited and latency-sensitive platforms, where efficiency must be improved without compromising predictive performance. While the Graph-Constrained Neural Multi-Objective Evolutionary Algorithm (GCNMOEA) has demonstrated advantages in sparsity promotion, runtime efficiency, and population diversity, its performance is hindered by reduced accuracy, unstable early-stage convergence, and lower hypervolume under moderate sparsity. To address these limitations, this study proposes Enhanced GCNMOEA, an improved variant that integrates accuracy-aware graph mutation, a two- phase dominance–decomposition selection mechanism, multifidelity evaluation, and adaptive diversity scheduling. These enhancements collectively strengthen structural preservation, reduce fitness noise, and improve convergence behaviour. Experimental evaluations on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 across LeNet-5, ResNet-18, and MobileNetV2 architectures demonstrate that the proposed variant achieves consistently higher accuracy, improved hypervolume, and reduced IGD variance, while maintaining the sparsity and runtime benefits of the baseline algorithm. The results confirm that Enhanced GCNMOEA deliverreal-worldsuperior Pareto-front quality and improved robustness, making it a strong candidate for real world edge and embedded neural network applications.