Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms

Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms” by Alfredo Cuesta-Infante, Roberto Santana, J. Ignacio Hidalgo, Concha Bielza, and Pedro Larrañaga. In Proceedings of the 2010 Congress on Evolutionary Computation CEC-2010, (Barcelone, Spain), 2010, pp. 1-8.

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 percentage of the benchmark functions.

BibTeX entry:

@inproceedings{Cuesta_et_al:2010,
   author = {Alfredo Cuesta-Infante and Roberto Santana and J. Ignacio
	Hidalgo and Concha Bielza and Pedro Larra{\~n}aga},
   title = {Bivariate empirical and n-variate {A}rchimedean copulas in
	estimation of distribution algorithms},
   booktitle = {Proceedings of the 2010 Congress on Evolutionary
	Computation CEC-2010},
   pages = {1-8},
   publisher = {IEEE},
   address = {Barcelone, Spain},
   year = {2010},
   url = {http://dx.doi.org/10.1109/CEC.2010.5586557}
}

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