Hyperparameter Optimization Using a Genetic Algorithm Considering Verification Time in a Convolutional Neural Network

Hyperparameter optimization is a very difficult problem in developing deep learning algorithms. In this paper, a genetic algorithm was applied to solve this problem. The accuracy and the verification time were considered by conducting a fitness evaluation. The algorithm was evaluated by using a simp...

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Bibliographic Details
Published inJournal of electrical engineering & technology Vol. 15; no. 2; pp. 721 - 726
Main Authors Han, Ji-Hoon, Choi, Dong-Jin, Park, Sang-Uk, Hong, Sun-Ki
Format Journal Article
LanguageEnglish
Published Singapore Springer Singapore 01.03.2020
Subjects
Electrical Engineering
Electrical Machines and Networks
Electronics and Microelectronics
Engineering
Instrumentation
Original Article
Power Electronics
MNIST
Hyper-parameter optimization
Genetic algorithm
Motor fault diagnosis
Convolutional neural network
Deep learning algorithm
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Summary:Hyperparameter optimization is a very difficult problem in developing deep learning algorithms. In this paper, a genetic algorithm was applied to solve this problem. The accuracy and the verification time were considered by conducting a fitness evaluation. The algorithm was evaluated by using a simple model that has a single convolution layer and a single fully connected layer. A model with three layers was used. The MNIST dataset and a motor fault diagnosis dataset were used to train the algorithm. The results show that the method is useful for reducing the training time.
ISSN:1975-0102
2093-7423
DOI:10.1007/s42835-020-00343-7