Understanding Selection and Diversity for Evolution of Spiking Recurrent Neural Networks

Catherine D. Schuman, Grant Bruer, Aaron R. Young, Mark Dean, James S. Plank

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

1 Scopus citations

Abstract

Evolutionary optimization or genetic algorithms have been used to optimize a variety of neural network types, including spiking recurrent neural networks, and are attractive for many reasons. However, a key impediment to their widespread use is the potential for slow training times and failure to converge to a good fitness value in a reasonable amount of time. In this work, we evaluate the effect of different selection algorithms on the performance of an evolutionary optimization method for designing spiking recurrent neural networks, including those that are meant to be deployed in a neuromorphic system. We propose a selection approach that utilizes a richer understanding of the fitness of an individual network to inform the selection process and to promote diversity in the population. We show that including this feature can provide a significant increase in performance over utilizing a standard selection approach.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
DOIs
StatePublished - Oct 10 2018
Externally publishedYes
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: Jul 8 2018Jul 13 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period07/8/1807/13/18

Funding

Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
DOE Office of ScienceDE-AC05-00OR22725
U. S. Department of Energy
Oak Ridge National Laboratory
Air Force Research LaboratoryFA8750-16-1-0065

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