Genetic Algorithms for Evolving Deep Neural Networks

Abstract

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.

References in corpus (1)

• Improving neural networks by preventing co-adaptation of feature detectors

Cited by in corpus (5)

• Gradient Descent based Optimization Algorithms for Deep Learning Models Training
• Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach
• GACNN: Training Deep Convolutional Neural Networks with Genetic Algorithm
• Neuroevolution in Deep Learning: The Role of Neutrality
• Training Stacked Denoising Autoencoders for Representation Learning