Global artificial bee colony algorithm for boolean function classification

Authors:
Habib Shah;Rozaida Ghazali;Nazri Mohd Nawi
Affiliations:
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat., Johor, Malaysia;Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat., Johor, Malaysia;Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat., Johor, Malaysia
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2013

Citing 13
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Abstract

This paper proposed Global Artificial Bee Colony algorithm for training Neural Network (NN), which is a globalised form of standard Artificial Bee Colony algorithm. NN trained with the standard backpropagation (BP) algorithm normally utilizes computationally intensive training algorithms. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome, GABC algorithm used in this work to train MLP learning for classification problem, the performance of GABC is benchmarked against MLP training with the typical BP, ABC and Particle swarm optimization for boolean function classification. The experimental result shows that MLP-GABC performs better than that standard BP, ABC and PSO for the classification task.