Optimizing the neural network hyperparameters utilizing genetic algorithm

Neural networks (NNs), as one of the most robust and efficient machine learning methods, have been commonly used in solving several problems. However, choosing proper hyperparameters (e.g. the numbers of layers and neurons in each layer) has a significant influence on the accuracy of these methods....

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Veröffentlicht in:	Journal of Zhejiang University. A. Science Jg. 22; H. 6; S. 407 - 426
Hauptverfasser:	Nikbakht, Saeid, Anitescu, Cosmin, Rabczuk, Timon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hangzhou Zhejiang University Press 01.06.2021 Springer Nature B.V Faculty of Civil Engineering,Ton Duc Thang University,Ho Chi Minh City,Vietnam Institute of Structural Mechanics,Bauhaus-Universit?t Weimar,Weimar 99423,Germany%Division of Computational Mechanics,Ton Duc Thang University,Ho Chi Minh City,Vietnam
Schlagworte:	Accuracy Algorithms Artificial neural networks Civil Engineering Classical and Continuum Physics Energy methods Engineering Genetic algorithms Industrial Chemistry/Chemical Engineering Learning algorithms Machine learning Mechanical Engineering Neural networks Neurons Optimization Stress concentration Stress distribution Timoshenko beams 遗传算法 Neural network (NN) Hyperparameters 机器学习 TP18 Genetic algorithm Machine learning 神经网络超参数 Neural network(NN)
ISSN:	1673-565X, 1862-1775
Online-Zugang:	Volltext
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