  In recent years, deep learning methods applying unsupervised learning to
train deep layers of neural networks have achieved remarkable results in
numerous fields. In the past, many genetic algorithms based methods have been
successfully applied to training neural networks. In this paper, we extend
previous work and propose a GA-assisted method for deep learning. Our
experimental results indicate that this GA-assisted approach improves the
performance of a deep autoencoder, producing a sparser neural network.
