Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

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.