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

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Abstract

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.

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Acknowledgements

This research was supported by the Korea Electric Power Corporation (Grant number: R18XA06-23).

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Correspondence to Sun-Ki Hong.

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Han, J., Choi, D., Park, S. et al. Hyperparameter Optimization Using a Genetic Algorithm Considering Verification Time in a Convolutional Neural Network. J. Electr. Eng. Technol. (2020) doi:10.1007/s42835-020-00343-7

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Keywords

  • Deep learning algorithm
  • Genetic algorithm
  • Hyper-parameter optimization
  • Motor fault diagnosis
  • Convolutional neural network
  • MNIST