Multi-Objectives Optimization (MOO)-Based NAS Approach

doi:10.1002/ima.70240

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Multi-Objectives Optimization (MOO)-Based NAS Approach

2025-11-15

Sana, Abass

Senhaji, Kaoutar

Amir Nakib *

DOI：10.1002/ima.70240

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Abstract

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This research dives into creating optimized neural network architectures tailored for image classification and medical image segmentation, with a particular emphasis on analyzing lung nodules using the LIDC-IDRI dataset. We introduce a hybrid approach to neural architecture search (NAS) that fuses convolutional neural networks (CNNs), transformer-based models, and custom-built graph architectures. Our evolutionary strategies employ genetic operations like crossover and mutation to gradually enhance these architectures, while the NSGA-III algorithm helps us navigate the tricky balance of multiple conflicting objectives. The goal of our method is to boost performance metrics such as the F1 score and classification accuracy, all while keeping an eye on minimizing computational complexity, which we measure in terms of FLOPS, parameter count, and inference time. Our experiments demonstrate competitive performance on the CIFAR-10 and CIFAR-100 datasets, with promising results on the LIDC-IDRI segmentation task. This work aims to push the boundaries of automated model design, contributing to more efficient and effective deep learning architectures in both general and medical imaging contexts.

Keywords:

artificial neural networks (ANN)

computer vision (CV)

deep learning (DL)

multi-objective optimization (MOO)

neural architecture search (NAS)

NSGAIII