Scheduled System Maintenance on December 17th, 2014:
IEEE Xplore will be upgraded between 2:00 and 5:00 PM EST (18:00 - 21:00) UTC. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Cuesta-Infante, A. ; Felipe II Coll., Univ. Complutense de Madrid, Aranjuez, Spain ; Santana, R. ; Hidalgo, J.I. ; Bielza, C.
more authors

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.

Published in:

Evolutionary Computation (CEC), 2010 IEEE Congress on

Date of Conference:

18-23 July 2010