CLC number: TP18
On-line Access: 2021-06-21
Received: 2020-08-25
Revision Accepted: 2020-12-21
Crosschecked: 2021-05-20
Cited: 0
Clicked: 3824
Citations: Bibtex RefMan EndNote GB/T7714
Saeid Nikbakht, Cosmin Anitescu, Timon Rabczuk. Optimizing the neural network hyperparameters utilizing genetic algorithm[J]. Journal of Zhejiang University Science A, 2021, 22(6): 407-426.
@article{title="Optimizing the neural network hyperparameters utilizing genetic algorithm",
author="Saeid Nikbakht, Cosmin Anitescu, Timon Rabczuk",
journal="Journal of Zhejiang University Science A",
volume="22",
number="6",
pages="407-426",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000384"
}
%0 Journal Article
%T Optimizing the neural network hyperparameters utilizing genetic algorithm
%A Saeid Nikbakht
%A Cosmin Anitescu
%A Timon Rabczuk
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 6
%P 407-426
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000384
TY - JOUR
T1 - Optimizing the neural network hyperparameters utilizing genetic algorithm
A1 - Saeid Nikbakht
A1 - Cosmin Anitescu
A1 - Timon Rabczuk
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 6
SP - 407
EP - 426
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000384
Abstract: 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. Therefore, a considerable number of studies have been carried out to optimize the NN hyperparameters. In this study, the genetic algorithm is applied to NN to find the optimal hyperparameters. Thus, the deep energy method, which contains a deep neural network, is applied first on a Timoshenko beam and a plate with a hole. Subsequently, the numbers of hidden layers, integration points, and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these structures. Thus, applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples.
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