Convolutional Neural Networks Hyperparameters Optimization Using Sine Cosine Algorithm

The most challenging task in the machine learning domain is optimizing the hyperparameters in convolutional neural networks. This task is representative of NP-hard problems, and consequently, it is not possible to solve it by applying standard deterministic approaches in an acceptable amount of time. Additionally, convolutional neural networks’ hyperparameters must be optimized for each particular problem, as there is no solution that fits all possible applications. Swarm intelligence metaheuristics have been established as efficient optimizers, and this paper proposes the enhanced sine cosine algorithm to address the task of hyperparameters optimization. The experiments conducted in this research were executed with the CIFAR-10 benchmark dataset. The experimental results were analyzed and validated against other proven metaheuristics approaches, and it can be concluded that the presented enhanced sine cosine approach outperformed other methods included in this research.