Training Of Artificial Neural Network Using Metaheuristic Algorithm - Alwaisi, Shaimaa Safaa Ahmed; Baykan, Omer Kaan (International Journal of Intelligent Systems and Applications in Engineering, 2017)
This article clarify enhancing classification accuracy of Artificial Neural Network (ANN) by using metaheuristic optimization algorithm. Classification accuracy of ANN depends on the well-designed ANN model. Well-designed ANN model Based on the structure, activation function that are utilized for ANN nodes, and the training algorithm which are used to detect the correct weight for each node. In our paper we are focused on improving the set of synaptic weights by using Shuffled Frog Leaping metaheuristic optimization algorithm which are determine the correct weight for each node in ANN model. We used 10 well known datasets from UCI machine learning repository. In order to investigate the performance of ANN model we used datasets with different properties. These datasets have categorical, numerical and mixed properties. Then we compared the classification accuracy of proposed method with the classification accuracy of back propagation training algorithm. The results showed that the proposed algorithm performed better performance in the most used datasets.