|
Descripción
|
|
|---|---|
| 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. | |
|
Internacional
|
Si |
|
Nombre congreso
|
2010 IEEE Congress on Evolutionary Computation (CEC-2010) |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
Barcelona, España |
|
Revisores
|
Si |
|
ISBN o ISSN
|
978-1-4244-6909-3 |
|
DOI
|
10.1109/CEC.2010.5586557 |
|
Fecha inicio congreso
|
18/07/2010 |
|
Fecha fin congreso
|
23/07/2010 |
|
Desde la página
|
1 |
|
Hasta la página
|
8 |
|
Título de las actas
|
Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC-2010) |