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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fionn, M.: Multilayer perceptrons for classification and regression. Neurocomputing 2, 183–197 (1991)
Liao, S.-H., Wen, C.-H.: Artificial neural networks classification and clustering of methodologies and applications - literature analysis from 1995 to 2005. Expert Systems with Applications 32, 1–11 (2007)
Ghazali, R., et al.: Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network. Neurocomputing 72, 2359–2367 (2009)
Uncini, A.: Audio signal processing by neural networks. Neurocomputing 55, 593–625 (2003)
Kiranyaz, S., et al.: Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks 22, 1448–1462 (2009)
Ilonen, J., et al.: Differential Evolution Training Algorithm for Feed-Forward Neural Networks. Neural Processing Letters 17, 93–105 (2003)
Dorigo, M., Caro, G.D.: The Ant Colony Optimization Meta-Heuristic in New Ideas in Optimization. McGraw-Hill, England (1999)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Nawi, N.M., Ransing, R.S., Salleh, M.N.M., Ghazali, R., Hamid, N.A.: An Improved Back Propagation Neural Network Algorithm on Classification Problems. In: Zhang, Y., Cuzzocrea, A., Ma, J., Chung, K.-i., Arslan, T., Song, X. (eds.) DTA and BSBT 2010. CCIS, vol. 118, pp. 177–188. Springer, Heidelberg (2010)
Nawi, N.M., Ghazali, R., Salleh, M.N.M.: The Development of Improved Back-Propagation Neural Networks Algorithm for Predicting Patients with Heart Disease. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. LNCS, vol. 6377, pp. 317–324. Springer, Heidelberg (2010)
Rumelhart, D.E., et al.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Shah, H., et al.: Global Hybrid Ant Bee Colony Algorithm for Training Artificial Neural Networks. Presented at the International Conference on Computational Science and Applications, Brazil (2012)
Shah, H., Ghazali, R., Nawi, N.M.: Hybrid Ant Bee Colony Algorithm for Volcano Temperature Prediction. In: Chowdhry, B.S., Shaikh, F.K., Hussain, D.M.A., Uqaili, M.A. (eds.) IMTIC 2012. CCIS, vol. 281, pp. 453–465. Springer, Heidelberg (2012)
Karaboga, D., Akay, B., Ozturk, C.: Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 318–329. Springer, Heidelberg (2007)
Shah, H., Ghazali, R.: Prediction of Earthquake Magnitude by an Improved ABC-MLP. In: Developments in E-systems Engineering (DeSE), pp. 312–317 (2011)
Peng, G., et al.: Global artificial bee colony search algorithm for numerical function optimization. In: 2011 Seventh International Conference on Natural Computation (ICNC), pp. 1280–1283 (2011)
Shah, H., et al.: G-HABC Algorithm for Training Artificial Neural Networks. International Journal of Applied Metaheuristic Computing 3, 20 (2012)
Stork, D.G., Allen, J.D.: How to solve the N-bit parity problem with two hidden units. Neural Networks 5, 923–926 (1992)
Iyoda, E.M., et al.: A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron. Neural Processing Letters 18, 233–238 (2003)
Rosenblatt, F.: The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 386–408 (1958)
Rumelhart, D.E., et al.: Parallel distributed processing: Psychological and biological models. MIT Press (1986)
Hieu Trung, H., Yonggwan, W.: Evolutionary algorithm for training compact single hidden layer feedforward neural networks. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008, pp. 3028–3033 (2008)
Bonabeau, E., et al.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)
Karaboga, D., Kalinli, A.: Training recurrent neural networks for dynamic system identification using parallel tabu search algorithm. In: Proceedings of the 1997 IEEE International Symposium on Intelligent Control, pp. 113-118 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shah, H., Ghazali, R., Nawi, N.M. (2013). Global Artificial Bee Colony Algorithm for Boolean Function Classification. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-36546-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36545-4
Online ISBN: 978-3-642-36546-1
eBook Packages: Computer ScienceComputer Science (R0)