Metaheuristic Algorithms for Convolution Neural Network

• Published in: Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 182 - 194-012
• Main Authors: Rere, L. M. Rasdi, Arymurthy, Aniati Murni, Fanany, Mohamad Ivan
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01-01-2016; Hindawi Publishing Corporation; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Brain - physiology; Brain Mapping; Computer Simulation; Convolution; Datasets; Experiments; Heuristic methods; Heuristic programming; Heuristics - physiology; Higher education; Humans; Laboratories; Learning; Mathematical research; Methods; Neural networks; Neural Networks (Computer); Optimization; Optimization techniques; Signal processing; Strategy
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2016/1537325

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).