Genetic Algorithms for Evolving Deep Neural Networks
This is an incremental improvement for researchers in deep learning and genetic algorithms.
The paper tackles the problem of training deep neural networks by proposing a GA-assisted method, which improves the performance of a deep autoencoder and produces a sparser neural network.
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.