Metaheuristic Algorithms for Convolution Neural Network
This work addresses the optimization of CNNs for researchers in deep learning, but it is incremental as it applies existing metaheuristic methods to a known problem.
The paper tackled the problem of optimizing Convolutional Neural Networks (CNNs) by implementing three metaheuristic algorithms (simulated annealing, differential evolution, and harmony search) to improve accuracy, resulting in an accuracy improvement of up to 7.14% on MNIST and CIFAR datasets, albeit with increased computation time.
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).