PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Evolving Scale-Free Topologies using a Gene Regulatory Network Model
Miguel Nicolau and Marc Schoenauer
In: CEC'08, 1-6 June 2008, Hong-Kong.

Abstract

A novel approach to generating scale-free network topologies is introduced, based on an existing artificial Gene Regulatory Network model, from which the underlying regulation network is extracted. 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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3933
Deposited By:Marc Schoenauer
Deposited On:25 February 2008