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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 1537325, 13 pages
Research Article

Metaheuristic Algorithms for Convolution Neural Network

1Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia
2Computer System Laboratory, STMIK Jakarta STI&K, Jakarta 12140, Indonesia

Received 29 January 2016; Revised 15 April 2016; Accepted 10 May 2016

Academic Editor: Martin Hagan

Copyright © 2016 L. M. Rasdi Rere et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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).