A hybrid differential evolution algorithm for parameter tuning of evolving spiking neural network

Abstract

In this paper, differential evolution (DE) has been utilised to solve the problem of tuning the parameters of evolving spiking neural network (ESNN) manually. As ESNN is sensitive to its parameters as other models, optimal integration of parameters leads to better classification accuracy. A hybrid differential evolution for parameter tuning of evolving spiking neural network (DEPT-ESNN) is presented for parameter optimisation for determining the optimal number of evolving spiking neural network (ESNN) parameters: modulation factor (Mod), similarity factor (Sim) and threshold factor (C). The best values of parameters are adaptively selected by differential evolution (DE) to avoid selecting suitable values for a particular problem by trial-and-error approach. Several standard datasets from UCI machine learning are used for evaluating the performance of this hybrid model. It has been found that the classification accuracy and other performance measures can be increased by using hybrid method with differential evolution DEPT-ESNN.