Benchmarking Many-Objective Topology and Weight Evolution of Neural Networks: A Study with NEWS/D

Adham Salih, Amiram Moshaiov

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

3 Scopus citations

Abstract

This study aims to provide procedures and benchmark problems to test the optimization capabilities of algorithms for Many-Objective Topology and Weight Evolution of Artificial Neural Networks (MaO-TWEANNs). In particular, the proposed benchmarks are based on a combination of continuous functions that have commonly been used to test single-objective optimization algorithms. In addition, this paper applies the proposed procedures and benchmark problems to evaluate NEWS/D, which is such an algorithm that has recently been introduced. The results of this study validate the optimization capabilities of NEWS/D for MaO-TWEANN problems with continuous outputs.

Original languageEnglish
Title of host publication2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728190488
DOIs
StatePublished - 2021
Event2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
Duration: 5 Dec 20217 Dec 2021

Publication series

Name2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/TerritoryUnited States
CityOrlando
Period5/12/217/12/21

Keywords

  • Artificial Neural Networks
  • Decomposition Approach
  • Many-objective Optimization
  • Neural Architecture Search
  • Neuro-evolution
  • Topology and Weight Evolution

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