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On-line Access: 2021-06-21

Received: 2020-08-25

Revision Accepted: 2020-12-21

Crosschecked: 2021-05-20

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Saeid Nikbakht


Timon Rabczuk


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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.6 P.407-426


Optimizing the neural network hyperparameters utilizing genetic algorithm

Author(s):  Saeid Nikbakht, Cosmin Anitescu, Timon Rabczuk

Affiliation(s):  Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Vietnam; more

Corresponding email(s):   timon.rabczuk@tdtu.edu.vn

Key Words:  Machine learning, Neural network (NN), Hyperparameters, Genetic algorithm

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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.

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%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
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%P 407-426
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000384

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
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2000384

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.


创新点:1. 为了提高DEM的准确性,各种超参数组合被输入遗传算法(GA)并找到最佳组合.2. 为了防止重复计算以及提高这种元启发式算法的效率,GA过程中还考虑了超参数组合的禁忌列表.
方法:1. 实施非均匀有理样条(NURBS)以生成穿过结构体和边界的积分点.2. 采用DEM计算位移和应力分布.3. 利用遗传算法优化DEM的超参数,以对模型在预测结构内应力和位移传播的准确性方面具有显着影响.
结论:1. 在不同的优化器和激活函数中,Adam和L-BFGS-B方法以及ReLU2函数的组合使得DEM模型的准确率最高.2. 其他对模型预测准确性有影响的超参数包括隐藏层的数量、每层神经元的数量以及通过上述结构集成的点数.3. 优化DEM的超参数可以使相对应变能误差降低近50%,提高了DEM模型对应力和位移分布的预测能力.


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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