Genetic algorithms for evolving deep neural networks

Omid E. David, Iddo Greental

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

111 Scopus citations

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.

Original languageEnglish
Title of host publicationGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages1451-1452
Number of pages2
ISBN (Print)9781450328814
DOIs
StatePublished - 2014
Event16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion - Vancouver, BC, Canada
Duration: 12 Jul 201416 Jul 2014

Publication series

NameGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference

Conference

Conference16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion
Country/TerritoryCanada
CityVancouver, BC
Period12/07/1416/07/14

Keywords

  • Autoencoders
  • Deep learning
  • Genetic algorithms
  • Neural networks

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