@inproceedings{56f3203b4b1b4664a1beb5619de15a94,
title = "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.",
keywords = "Autoencoders, Deep learning, Genetic algorithms, Neural networks",
author = "David, {Omid E.} and Iddo Greental",
year = "2014",
doi = "10.1145/2598394.2602287",
language = "אנגלית",
isbn = "9781450328814",
series = "GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery",
pages = "1451--1452",
booktitle = "GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference",
note = "16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion ; Conference date: 12-07-2014 Through 16-07-2014",
}