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Memorias de investigación
Communications at congresses:
Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms
Year:2010
Research Areas
  • Artificial intelligence
Information
Abstract
This paper investigates the use of empirical and Archimedean copulas as probabilistic models of continuous estimation of distribution algorithms (EDAs). A method for learning and sampling empirical bivariate copulas to be used in the context of n-dimensional EDAs is first introduced. Then, by using Archimedean copulas instead of empirical makes possible to construct n-dimensional copulas with the same purpose. Both copula-based EDAs are compared to other known continuous EDAs on a set of 24 functions and different number of variables. Experimental results show that the proposed copula-based EDAs achieve a better behaviour than previous approaches in a 20% of the benchmark functions.
International
Si
Congress
2010 IEEE Congress on Evolutionary Computation (CEC-2010)
960
Place
Barcelona, España
Reviewers
Si
ISBN/ISSN
978-1-4244-6909-3
10.1109/CEC.2010.5586557
Start Date
18/07/2010
End Date
23/07/2010
From page
1
To page
8
Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC-2010)
Participants
  • Autor: Roberto Santana Hermida (UPM)
  • Participante: Alfredo Cuesta-Infante (Universidad Complutense de Madrid)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Participante: J. Ignacio Hidalgo (Universidad Complutense de Madrid)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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