(untitled) (gjstudies, 2025)
This paper presents a novel neural architecture search (NAS) framework that optimizes convolutional neural networks for both accuracy andcomputational eciency. Traditional NAS methods often prioritize accuracy at the expense of computational requirements, making them impractical for resource-constrained environments. Our approach employsa multi-objective optimization strategy that simultaneously maximizesclassication accuracy while minimizing computational cost, measured in oating-point operations (FLOPs). We introduce a hierarchical searchspace that enables ecient exploration of architectural variations and implement a modied evolutionary algorithm with adaptive mutation rates.Experimental results on CIFAR-10 and ImageNet datasets demonstratethat our method discovers architectures that achieve competitive accuracywith state-of-the-art models while reducing computational requirementsby 35-60