TY - JOUR
T1 - Metaheuristic Algorithms for Convolution Neural Network
AU - Rasdi Rere, L. M.
AU - Fanany, Mohamad Ivan
AU - Arymurthy, Aniati Murni
N1 - Publisher Copyright:
© 2016 L. M. Rasdi Rere et al.
PY - 2016
Y1 - 2016
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=84976464001&partnerID=8YFLogxK
U2 - 10.1155/2016/1537325
DO - 10.1155/2016/1537325
M3 - Article
C2 - 27375738
AN - SCOPUS:84976464001
SN - 1687-5265
VL - 2016
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 1537325
ER -