Rere L M Rasdi, Fanany Mohamad Ivan, Arymurthy Aniati Murni
Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia; Computer System Laboratory, STMIK Jakarta STI&K, Jakarta 12140, Indonesia.
Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia.
Comput Intell Neurosci. 2016;2016:1537325. doi: 10.1155/2016/1537325. Epub 2016 Jun 8.
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).
典型的现代优化技术通常要么是启发式的，要么是元启发式的。这种技术已经成功解决了科学、工程和工业研究领域中的一些优化问题。然而，元启发式方法在著名的深度学习方法卷积神经网络（CNN）上提高准确性的实施策略仍鲜有研究。深度学习涉及一种机器学习技术，其目标是更接近人工智能的目标，即创建一台能够成功执行人类可以执行的任何智力任务的机器。在本文中，我们提出了三种流行的元启发式方法，即模拟退火、差分进化和和声搜索的实施策略，以优化CNN。评估并比较了这些元启发式方法在对MNIST和CIFAR数据集进行分类时优化CNN的性能。此外，还将所提出的方法与原始CNN进行了比较。虽然所提出的方法显示计算时间有所增加，但其准确性也得到了提高（提高了7.14%）。
