Title: New Learning Approach By Genetic Algorithm In A Convolutional Neural Network For Pattern Recognition
Authors: Mohammad Ali Mehrolhassani, Majid Mohammadi
Keywords: Deep learning, Supervised Learning, Optimization, Computer vision
TL;DR: Implement new approach without exerting backpropagation in learning of CNN is useful for parallel processing Like GPU.
Abstract:
Almost all of the presented articles in the CNN  are based on the error backpropagation algorithm and calculation of derivations of error, our innovative proposal refers to engaging TICA  filters and NSGA-II  genetic algorithms to train the LeNet-5 CNN network. Consequently, genetic algorithm updates the weights of LeNet-5 CNN network similar to chromosome update. In our approach the weights of LeNet-5 are obtained in two stages. The first is pre-training and the second is fine-tuning. As a result, our approach impacts in learning task.
Venue: Submitted to ICLR 2017