Evolving Scale-Free Topologies using a Gene Regulatory Network Model
|Title:||Evolving Scale-Free Topologies using a Gene Regulatory Network Model||Authors:||Nicolau, Miguel
|Permanent link:||http://hdl.handle.net/10197/8261||Date:||6-Jun-2008||Online since:||2017-01-13T15:31:00Z||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.||Funding Details:||European Commission||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 computation||DOI:||10.1109/cec.2008.4631305||Language:||en||Status of Item:||Peer reviewed||Conference Details:||IEEE Congress on Evolutionary Computation (CEC 2008) Hong-Kong, China, 1-6 June 2008|
|Appears in Collections:||Business Research Collection|
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