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Evolving Scale-Free Topologies using a Gene Regulatory Network Model

Miguel Nicolau 1, 2, * Marc Schoenauer 1, 2 
* Corresponding author
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
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.
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Submitted on : Thursday, October 9, 2008 - 2:04:10 PM
Last modification on : Tuesday, October 25, 2022 - 4:17:07 PM
Long-term archiving on: : Friday, June 4, 2010 - 12:24:35 PM


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  • HAL Id : inria-00327755, version 1


Miguel Nicolau, Marc Schoenauer. Evolving Scale-Free Topologies using a Gene Regulatory Network Model. IEEE Congress on Evolutionary Computation, Jun 2008, Hong-Kong, China. pp.3748--3755. ⟨inria-00327755⟩



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