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Evolving Scale-Free Topologies using a Gene Regulatory Network Model

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Download nicolau08a.pdf2.63 MB
Author(s)
Nicolau, Miguel 
Schoenauer, Marc 
Uri
http://hdl.handle.net/10197/8261
Date Issued
06 June 2008
Date Available
13T15:31:00Z January 2017
Abstract
A novel approach to generating scale-free network topologies is introduced, based on an existing artificial Gene Regulatory Network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an Evolutionary Computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also exhibit a much higher potential for evolution.
Sponsorship
European Commission
Other Sponsorship
Sixth European Research Framework
Type of Material
Conference Publication
Publisher
IEEE
Start Page
3747
End Page
3754
Copyright (Published Version)
2008 IEEE
Keywords
  • Scale-free topologies...

  • Complex networks

  • Evolutionary computat...

DOI
10.1109/cec.2008.4631305
Language
English
Status of Item
Peer reviewed
Description
IEEE Congress on Evolutionary Computation (CEC 2008) Hong-Kong, China, 1-6 June 2008
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Business Research Collection
Scopus© citations
3
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Feb 1, 2023
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1362
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Downloads
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