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

Abstract 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.

[1]  Jing J. Liang,et al.  A self-adaptive global best harmony search algorithm for continuous optimization problems , 2010, Appl. Math. Comput..

[2]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[3]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[4]  Alireza Sadeghian,et al.  A Variation of Particle Swarm Optimization for Training of Artificial Neural Networks , 2010 .

[5]  Christian Blum,et al.  An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training , 2007, Neural Computing and Applications.

[6]  Derviş Karaboğa,et al.  NEURAL NETWORKS TRAINING BY ARTIFICIAL BEE COLONY ALGORITHM ON PATTERN CLASSIFICATION , 2009 .

[7]  M. Bialko,et al.  Training of artificial neural networks using differential evolution algorithm , 2008, 2008 Conference on Human System Interactions.

[8]  Duc Truong Pham,et al.  Computational Intelligence: for Engineering and Manufacturing , 2007 .

[9]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[10]  Mahamed G. H. Omran,et al.  Global-best harmony search , 2008, Appl. Math. Comput..

[11]  Ali Kattan,et al.  Harmony Search Based Supervised Training of Artificial Neural Networks , 2010, 2010 International Conference on Intelligent Systems, Modelling and Simulation.

[12]  Randall S. Sexton,et al.  Comparing backpropagation with a genetic algorithm for neural network training , 1999 .

[13]  Lale Özbakir,et al.  A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner , 2010, Appl. Soft Comput..