Fine-Tuning Convolutional Neural Networks Using Harmony Search

Deep learning-based approaches have been paramount in the last years, mainly due to their outstanding results in several application domains, that range from face and object recognition to handwritten digits identification. Convolutional Neural Networks (CNN) have attracted a considerable attention since they model the intrinsic and complex brain working mechanism. However, the huge amount of parameters to be set up may turn such approaches more prone to configuration errors when using a manual tuning of the parameters. Since only a few works have addressed such shortcoming by means of meta-heuristic-based optimization, in this paper we introduce the Harmony Search algorithm and some of its variants for CNN optimization, being the proposed approach validated in the context of fingerprint and handwritten digit recognition, as well as image classification.