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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Han, JH., Choi, DJ., Park, SU. et al. Hyperparameter Optimization Using a Genetic Algorithm Considering Verification Time in a Convolutional Neural Network. J. Electr. Eng. Technol. 15, 721–726 (2020). https://doi.org/10.1007/s42835-020-00343-7
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DOI: https://doi.org/10.1007/s42835-020-00343-7