PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On the Evolution of Scale-Free Topologies with a Gene Regulatory Network Model
Miguel Nicolau and Marc Schoenauer
BioSystems Journal Volume 93, Number 3, pp. 137-148, 2009.

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 statisticalmeasures. 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 require only a few evolutionary cycles to achieve a satisfactory error value.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6893
Deposited By:Marc Schoenauer
Deposited On:10 April 2010