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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Bibliographic Details
Published in:Journal of Zhejiang University. A. Science Vol. 22; no. 6; pp. 407 - 426
Main Authors: Nikbakht, Saeid, Anitescu, Cosmin, Rabczuk, Timon
Format: Journal Article
Language:English
Published: 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
Subjects:
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 Access:Get full text
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Summary: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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ISSN:1673-565X
1862-1775
DOI:10.1631/jzus.A2000384