alexa Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks
ISSN: 2090-4908

International Journal of Swarm Intelligence and Evolutionary Computation
Open Access

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Research Article

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

Abdulrazak Yahya Saleh*, Siti Mariyam Shamsuddin and Haza Nuzly Abdull Hamed

UTM Big Data Centre, Universiti Teknologi Malaysia

Corresponding Author:
Abdulrazak Yahya Saleh
UTM Big Data Centre,Universiti Teknologi Malaysia
UTM Skudai, Johor, Malysia
Tel: 601137090573
Email: [email protected]

Received date: January 19, 2016; Accepted date: February 22, 2016; Published date: February 27, 2016

Citation: Saleh AY, Shamsuddin SM, Hamed HNA (2016) Memetic Harmony Search Algorithm Based on Multi-objective Differential Evolution of Evolving Spiking Neural Networks. Int J Swarm Intel Evol Comput 5:130. doi:10.4172/2090- 4908.1000130

Copyright: © 2016 Saleh AY, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many realworld optimization problems include several contradictory objectives. Rather than single optimization, Multi-Objective Optimization (MOO) can be utilized as a set of optimal solutions to solve these problems. In this paper, Harmony Search (HS) and memetic approach was used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODEESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure.

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