Harmony Search Based Supervised Training of Artificial Neural Networks

This paper presents a novel technique for the supervised training of feed-forward artificial neural networks (ANN) using the Harmony Search (HS) algorithm. HS is a stochastic meta-heuristic that is inspired from the improvisation process of musicians. Unlike Backpropagation, HS is non-trajectory driven. By modifying an existing improved version of HS & adopting a suitable ANN data representation, we propose a training technique where two of HS probabilistic parameters are determined dynamically based on the best-to-worst (BtW) harmony ratio in the current harmony memory instead of the improvisation count. This would be more suitable for ANN training since parameters and termination would depend on the quality of the attained solution. We have empirically tested and verified our technique by training an ANN with a benchmarking problem. In terms of overall training time and recognition, our results have revealed that our method is superior to both the original improved HS and standard Backpropagation.