Hybrid Algorithm for the Optimization of Training Convolutional Neural Network

The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN). Although stochastic gradient descend (SGD) is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA) are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.

@article{Albeahdili2015,
  title     = {Hybrid Algorithm for the Optimization of Training Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {10},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Hayder M. Albeahdili and Tony Han and Naz E. Islam},
  doi       = {10.14569/IJACSA.2015.061011},
  url       = {https://doi.org/10.14569/IJACSA.2015.061011}
}