UTHM Institutional Repository

Global artificial bee colony algorithm for boolean function classification

Shah, Habib and Ghazali, Rozaida and Mohd Nawi, Nazri (2013) Global artificial bee colony algorithm for boolean function classification. In: ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems, 18-20 March 2013, Kuala Lumpur, Malaysia. (Unpublished)

Full text not available from this repository.

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: artificial bee colony algorithm; back propagation; global artificial bee colony algorithm
Subjects: Q Science > QA Mathematics > QA75 Calculating machines
Depositing User: Normajihan Abd. Rahman
Date Deposited: 13 Aug 2018 03:42
Last Modified: 13 Aug 2018 03:42
URI: http://eprints.uthm.edu.my/id/eprint/3747
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item