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An Optimized Convolutional Neural Network Architecture Based on Evolutionary Ensemble Learning

Computers, Materials & Continua 2021年第12期69卷 3813-3828页

作者： Qasim M.Zainel Murad B.K.horsheed Saad Darwish Amr A.Ahmed College of Physical Education and Sports Sciences University of KirkukKirkuk36001Iraq College of Administration&Economics University of KirkukKirkuk36001Iraq Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandriaEgypt Department of Computer Engineering Alexandria Higher Institute of Engineering&Technology(AIET)AlexandriaEgypt

Convolutional Neural Networks(CNNs)models succeed in vast *** are available in a variety of topologies and *** challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the *** proposed framework to automated design is aimed at resolving this *** proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best *** comparison to the co-authored work,our proposed framework is concerned with creating lightweight architectures with a limited number of parameters while retaining a high degree of validity accuracy utilizing an ensemble learning *** architecture is intended to operate on low-resource machines,rendering it ideal for implementation in a number of *** common benchmark image datasets are used to test the proposed framework,and it is compared to peer competitors’work utilizing a range of parameters,including accuracy,the number of model parameters used,the number of GPUs used,and the number of GPU days needed to complete the *** experimental findings demonstrated a significant advantage in terms of GPU days,accuracy,and the number of parameters in the discovered model.

关键词： Convolutional neural networks genetic algorithm automatic model design ensemble learning

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