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Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

Zhiwei Ye1, Ziqian Fang1, Zhina Song1,*, Haigang Sui2, Chunyan Yan1, Wen Zhou1, Mingwei Wang1

1 School of Computer Science and Technology, Hubei University of Technology, Wuhan, 430068, China
2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

* Corresponding Author: Zhina Song. Email: email

Intelligent Automation & Soft Computing 2023, 37(2), 2019-2035. https://doi.org/10.32604/iasc.2023.039949

Abstract

Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of easy implementation, less parameters and fast convergence. In the paper, genetic search is combined with the hybrid rice optimization algorithm (GHRO) and employed to obtain the optimal hyperparameter of the capsule network automatically, that is, a probability search technique and a hybridization strategy belong with the primary HRO. Thirteen benchmark functions are used to evaluate the performance of GHRO. Furthermore, the MNIST, Chest X-Ray (pneumonia), and Chest X-Ray (COVID-19 & pneumonia) datasets are also utilized to evaluate the capsule network learnt by GHRO. The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network, which is able to boost the performance of the capsule network on image classification.

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APA Style
Ye, Z., Fang, Z., Song, Z., Sui, H., Yan, C. et al. (2023). Hyperparameter optimization for capsule network based modified hybrid rice optimization algorithm. Intelligent Automation & Soft Computing, 37(2), 2019-2035. https://doi.org/10.32604/iasc.2023.039949
Vancouver Style
Ye Z, Fang Z, Song Z, Sui H, Yan C, Zhou W, et al. Hyperparameter optimization for capsule network based modified hybrid rice optimization algorithm. Intell Automat Soft Comput . 2023;37(2):2019-2035 https://doi.org/10.32604/iasc.2023.039949
IEEE Style
Z. Ye et al., "Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm," Intell. Automat. Soft Comput. , vol. 37, no. 2, pp. 2019-2035. 2023. https://doi.org/10.32604/iasc.2023.039949



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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