  Convolutional neural network (CNN) is one of the most prominent architectures
and algorithm in Deep Learning. It shows a remarkable improvement in the
recognition and classification of objects. This method has also been proven to
be very effective in a variety of computer vision and machine learning
problems. As in other deep learning, however, training the CNN is interesting
yet challenging. Recently, some metaheuristic algorithms have been used to
optimize CNN using Genetic Algorithm, Particle Swarm Optimization, Simulated
Annealing and Harmony Search. In this paper, another type of metaheuristic
algorithms with different strategy has been proposed, i.e. Microcanonical
Annealing to optimize Convolutional Neural Network. The performance of the
proposed method is tested using the MNIST and CIFAR-10 datasets. Although
experiment results of MNIST dataset indicate the increase in computation time
(1.02x - 1.38x), nevertheless this proposed method can considerably enhance the
performance of the original CNN (up to 4.60\%). On the CIFAR10 dataset,
currently, state of the art is 96.53\% using fractional pooling, while this
proposed method achieves 99.14\%.
