An integrated harmony search algorithm-based multi-objective differential evolution of evolving spiking neural network

In this paper, an integrated harmony search algorithm based on multi-objective differential evolution of evolving spiking neural network (HSMODE-ESNN) is presented to determine the optimal pre-synaptic neurons (network structure) and accuracy performance for classification problems simultaneously. This proposed method uses the harmony search (HS) algorithm in selecting the offspring by using all individuals rather than two in differential evolution (DE). This feature enhances the flexibility of the HS algorithm in producing better solutions which is utilised to overcome the disadvantage of DE. Several standard datasets from UCI machine learning are used for evaluating the performance of this hybrid model. The experimental results have proven that the hybrid (HSMODE-ESNN) gives better results in terms of accuracy and complexity. © 2016 Inderscience Enterprises Ltd.