2026· Journal of Independent Studies and Research - Computing· Vol 24· 0 citations
TL;DR
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 that delivers real-worldsuperior Pareto-front quality and improved robustness, making it a strong candidate for real world edge and embedded neural network applications.
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.
Ensemble-GNAS is proposed, a novel framework that integrates evolutionary GNAS with ensemble learning to address limitations of existing graph NAS algorithms and outperforms the state-of theart methods in both classification accuracy and performance stability.
Weifeng Guo, Pengyu Wang, Ying Bi et al.· IEEE Transactions on Neural...· 0 citations
The proposed DD-AG2m alternates between AG2m optimization on the original (global) graph and AG2m optimization on the partitioned graphs, and introduces a two-level variant that performs global optimization steps on a coarse graph obtained by randomly subsampling nodes within each subdomain.
Laurynas Varnas, Julien Herrmann, Alexander Heinlein et al.· 0 citations
A dual-objective surrogate-guided ensemble search is introduced: candidate architectures are represented as directed acyclic graphs, and two surrogate models are trained independently to estimate predictive accuracy and diversity potential and guide an NES framework that efficiently identifies architectures that are both individually strong and collectively diverse.
Alexandr Udeneev, Petr Babkin, O. Bakhteev· arXiv.org· 0 citations
Existing learning-based influence maximization frameworks rely heavily on complex neural architectures and continuous optimization over seed representations. We challenge this paradigm with SIMBA, a diffusion-model-agnostic framework pairing a lightweight neural surrogate with direct discrete search. SIMBA introduces three key components: 1) uniformly anchored node embeddings that eliminate initialization noise and encourage learning driven by graph topology and diffusion pattern, 2) a shallow two-layer graph neural network surrogate predicting final infection states, and 3) batched multi-swap simulated annealing that explores combinatorial seed space without gradients or continuous relaxation. By shifting compute from complex representation learning to effective discrete search, SIMBA drastically cuts time-to-solution while achieving superior influence spread and data efficiency. Our code is available at https://github.com/yl489/rethink-IM.
Deep neural networks often encounter non-convex optimization challenges during training due to the presence of local minima, saddle points, and complex loss surfaces. Existing optimization algorithms such as Adam and Stochastic Gradient Descent (SGD) offer complementary advantages—Adam provides faster convergence, while SGD tends to achieve better generalization. However, neither optimizer alone effectively balances both properties in non-convex settings. To address this limitation, this paper proposes a phase-switch hybrid optimization strategy that combines the strengths of Adam and SGD. The proposed method employs Adam during the initial phase of training to enable rapid convergence and efficient exploration of the loss landscape, and then switches to momentum-based SGD in the later phase to improve generalization and ensure stable convergence. The effectiveness of the proposed approach is evaluated on one benchmark dataset, MNIST dataset, under different learning rate settings. Experimental results demonstrate that the proposed method achieves performance that is superior or comparable to existing optimizers in terms of accuracy and loss minimization. These results indicate that the proposed hybrid optimization strategy provides a simple and effective solution for handling non-convex optimization problems in deep learning.
