A New Harmony Search Algorithm with Evolving Spiking Neural Network for Classification Problems


  • Abdulrazak Yahya Saleh FSKPM Faculty, University Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300 Sarawak, Malaysia.
  • Siti Mariyam Shamsuddin UTM Big Data Centre, Universiti Teknologi Malaysia (UTM), Skudai, 81310 Johor, Malaysia.
  • Haza Nuzly Abdull Hamed Soft Computing Research Group3, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, 81310 Johor, Malaysia.
  • Teh Chee Siong FSKPM Faculty, University Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300 Sarawak, Malaysia.
  • Mohd Kamal Othman FSKPM Faculty, University Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300 Sarawak, Malaysia.


Harmony Search, Classification, Spiking Neural Network, Evolving Spiking Neural Networks,


In this study, a new hybrid harmony search algorithm with evolving spiking neural network (NHS-ESNN) for classification issues has been demonstrated. Harmony search has been used to enhance the standard ESNN model. This new algorithm plays an effective role in improving the flexibility of the ESNN algorithm in creating superior solutions to conquer the disadvantages of ESNN in determining the best number of pre-synaptic neurons which is necessary in constructing the ESNN structure. Various standard data sets from UCI machine learning are utilised for examining the new model performance. It has been detected that the NHS-ESNN give competitive results in classification accuracy and other performance measures compared to the standard ESNN. More argumentation is provided to verify the effectiveness of the new model in classification issues.


F. Y. Ahmed, S. M. Shamsuddin, and S. Z. M. Hashim, Improved SpikeProp for using Particle Swarm Optimization (PSO), 2013.

T. M. Mitchell and T. Michell, Machine Learning, McGraw-Hill Series in Computer Science: McGraw-Hill Higher Education, New York, NY, USA, 1997.

W. Gerstner and W. M. Kistler, Spiking neuron models: Single neurons, populations, plasticity. Cambridge university press, 2002.

A. Belatreche, L. P. Maguire, M. Mcginnity, and Q. X. Wu, “Evolutionary design of spiking neural networks,” New Mathematics and Natural Computation, vol. 2, no. 03, pp. 237-253, 2006.

S. Dora, K. Subramanian, S. Suresh, and N. Sundararajan, “Development of a self-regulating evolving spiking neural network for classification problem,” Neurocomputing, vol. 171, pp. 1216-1229, 2016.

N. K. Kasabov, M. G. Doborjeh, and Z. G. Doborjeh, “Mapping, learning, visualization, classification, and understanding of fMRI Data in the NeuCube evolving spatiotemporal data machine of spiking neural networks,” IEEE transactions on neural networks and learning systems, vol. 28, no. 4, pp. 887-899, 2017.

S. Schliebs, M. Defoin-Platel, S. Worner, and N. Kasabov, “Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models,” Neural Networks, vol. 22, no. 5, pp. 623-632, 2009.

Hamed, Novel Integrated Methods of Evolving Spiking Neural Network and Particle Swarm Optimisation. Auckland University of Technology, 2012.

N. Kasabov, V. Feigin, Z.-G. Hou, Y. Chen, L. Liang, R. Krishnamurthi, M. Othman, and P. Parmar, “Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke,” Neurocomputing, vol. 134, pp. 269-279, 2014.

M. J. Watts, “A decade of Kasabov's evolving connectionist systems: a review,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on., vol. 39, no. 3, pp. 253-269, 2009.

M. R. Razfar, R. F. Zinati, and M. Haghshenas, “Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 52, no. 5-8, pp. 487-495, 2011.

M. A. Z. Soltani, A. T. Haghighat, H. Rashidi, and T. G. Chegini, “A Couple of Algorithms for K-Coverage Problem in Visual Sensor Networks,” International Conference on Communication Engineering and Networks, 2011.

S. Wysoski, L. Benuskova, and N. Kasabov, “On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition,” Artificial Neural Networks–ICANN 2006, pp. 61-70, 2006.

N. Kasabov, K. Dhoble, N. Nuntalid, and G. Indiveri, “Dynamic evolving spiking neural networks for on-line spatio-and spectrotemporal pattern recognition,” Neural Networks, vol. 41, pp. 188-201, 2013.

S. Schliebs and N. Kasabov, “Evolving spiking neural network—a survey,” Evolving Systems, vol. 4, no. 2, pp. 87-98, 2013.

N. Kasabov, Evolving spiking neural networks and neurogenetic systems for spatio-and spectro-temporal data modelling and pattern recognition. In Advances in Computational Intelligence, pp. 234-260. Springer, 2012.

S. M. Bohte, J. N. Kok, and H. La Poutre, “Error-backpropagation in temporally encoded networks of spiking neurons,” Neurocomputing, vol. 48, no. 1, pp. 17-37, 2002.

R. Batllori, C. Laramee, W. Land, and J. Schaffer, “Evolving spiking neural networks for robot control,” Procedia Computer Science, vol. 6, pp. 329-334, 2011.

A. Mohemmed, S. Schliebs, S. Matsuda, and N. Kasabov, “Training spiking neural networks to associate spatio-temporal input–output spike patterns,” Neurocomputing, vol. 107, pp. 3-10, 2013.

N. Murli, N. Kasabov, and B. Handaga, “Classification of fMRI Data in the NeuCube Evolving Spiking Neural Network Architecture,” In International Conference on Neural Information Processing, Springer International Publishing, pp. 421- 428, 2014.

N. Nuntalid, K. Dhoble, and N. Kasabov, “EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network,” In Neural information processing, Springer Berlin/Heidelberg, pp. 451-460, 2011.

Z. W. Geem, J. H. Kim, and G. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60-68, 2001.

X.-S. Yang, Harmony search as a metaheuristic algorithm. In Musicinspired harmony search algorithm, Springer, pp. 1-14, 2009.

A. Abdolalipour and A. Alibabaee, “Harmony Search algorithm,” Int. J. Acad. Res. Appl. Sci., vol. 1, no. 3, pp. 13-16, 2012.

O. Moh’d Alia and R. Mandava, “The variants of the harmony search algorithm: an overview,” Artificial Intelligence Review, vol. 36, no. 1, pp. 49-68, 2011.

K. S. Lee and Z. W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice,” Computer methods in applied mechanics and engineering, vol. 194, no. 36, pp. 3902-3933, 2005.

A. Y. Saleh, S. M. Shamsuddin, H. N. A. Hamed, “Memetic Harmony Search Algorithm Based on Multi-objective Differential Evolution of Evolving Spiking Neural Networks,” Int J Swarm Intel Evol Comput., vol. 5, p. 130, 2016.

D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," Evolutionary Computation, IEEE Transactions on, vol. 1, no. 1, pp. 67-82, 1997.

N. K. Kasabov, "NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data," Neural Networks, vol. 52, pp. 62-76, 2014.




How to Cite

Saleh, A. Y., Shamsuddin, S. M., Abdull Hamed, H. N., Siong, T. C., & Othman, M. K. (2017). A New Harmony Search Algorithm with Evolving Spiking Neural Network for Classification Problems. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-11), 23–26. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3176