Abstract—This paper proposes a new structure for Quantum-inspired Particle Swarm Optimization (QiPSO) to enhance feature and parameter optimization of Evolving Spiking Neural Networks (ESNN). The new Dynamic Quantum-inspired Particle Swarm Optimization (DQiPSO) will be integrated within ESNN where features and parameters are simultaneously and more efficiently optimized. The features are modeled as a quantum bit vector, where probability computation is added to perform the feature selection task. For the parameters, values are presented as real numbers. A hybrid particle structure is required for these two different data types. In addition, an improved search strategy has been introduced to find the most relevant features and eliminate irrelevant features on a synthetic dataset. The results show that the proposed optimizer structure yields promising outcomes in identifying the most relevant features, and obtaining the best combination of ESNN parameters with faster and more accurate classification.
Index Terms—Feature selection, particle swarm optimization, quantum computation, spiking neural networks.
H. N. A. Hamed is a member of Department of Information Systems, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia (e-mail: email@example.com)
N. Kasabov is the Foundation Director of KEDRI, and a Chair of Knowledge Engineering at the School of Computer and Information Sciences at AUT, Private Bag 92006, and Auckland 1142, New Zealand (e-mail: firstname.lastname@example.org).
S. M. Shamsuddin is a Head of the Soft Computing Research Group, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia (e-mail: email@example.com).
Cite: Haza Nuzly Abdull Hamed, Nikola Kasabov, and Siti Mariyam Shamsuddin, "Dynamic Quantum-Inspired Particle Swarm Optimization as Feature and Parameter Optimizer for Evolving Spiking Neural Networks," International Journal of Modeling and Optimization vol. 2, no. 3, pp. 187-191, 2012.