Self-adaptive global best harmony search algorithm for training neural networks

KULLUK S., ÖZBAKIR L., Baykasoglu A.

1st World Conference on Information Technology (WCIT), İstanbul, Türkiye, 6 - 10 Ekim 2010, cilt.3 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 3
  • Doi Numarası: 10.1016/j.procs.2010.12.048
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Neural networks, Harmony search, Machine learning, Meta-heuristics, Computational intelligence, OPTIMIZATION ALGORITHM
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This paper addresses the application of Self-adaptive Global Best Harmony Search (SGHS) algorithm for the supervised training of feed-forward neural networks (NNs). A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on two classification benchmarking problems. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm is compared with other harmony search algorithms and the standard back-propagation algorithm. The experiments presented that the proposed algorithm lends itself very well to training of NNs and it is also highly competitive with the compared methods. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.