Article ID: | iaor20132543 |
Volume: | 64 |
Issue: | 5 |
Start Page Number: | 748 |
End Page Number: | 761 |
Publication Date: | May 2013 |
Journal: | Journal of the Operational Research Society |
Authors: | Kulluk S |
Keywords: | classification, harmony search |
Neural networks (NNs) are one of the most widely used techniques for pattern classification. Owing to the most common back‐propagation training algorithm of NN being extremely computationally intensive and it having some drawbacks, such as converging into local minima, many meta‐heuristic algorithms have been applied to training of NNs. This paper presents a novel hybrid algorithm which is the integration of Harmony Search (HS) and Hunting Search (HuS) algorithms, called h–HS‐HuS, in order to train Feed‐Forward Neural Networks (FFNNs) for pattern classification. HS and HuS algorithms are recently proposed meta‐heuristic algorithms inspired from the improvisation process of musicians and hunting of animals, respectively. Harmony search builds up the main structure of the hybrid algorithm, and HuS forms the pitch adjustment phase of the HS algorithm. The performance proposed algorithm is compared to conventional and meta‐heuristic algorithms. Empirical tests are carried out by training NNs on nine widely used classification benchmark problems. The experimental results show that the proposed hybrid harmony‐hunting algorithm is highly capable of training NNs.