Peter Ndajah

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

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.