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Benchmarking an artificial neural network tuned by a genetic algorithm
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2012 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis starts with a brief introduction to neural networks and the tuning of neural networks using genetic algorithms. An improved genetic algorithm is benchmarked using the technical paper Proben1 as a starting point. The benefits of using a genetic algorithm as well as results of the benchmark tests in comparison to a resilient backpropagation algorithm are discussed. The improved genetic algorithm is not a universal solution to all classification problems. Even though it outperforms  the resilient backpropagation algorithm slightly in these benchmark tests more benchmarking on more vast solution domains must be performed.

Place, publisher, year, edition, pages
2012.
Series
UMNAD, 908
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-58253OAI: oai:DiVA.org:umu-58253DiVA: diva2:547519
Educational program
Bachelor of Science Programme in Computing Science
Uppsok
Technology
Supervisors
Examiners
Available from: 2012-08-28 Created: 2012-08-28 Last updated: 2012-09-07Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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