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Abstract
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| 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. | |
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International
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Si |
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Congress
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2010 IEEE Congress on Evolutionary Computation (CEC-2010) |
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960 |
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Place
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Barcelona, España |
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Reviewers
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Si |
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ISBN/ISSN
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978-1-4244-6909-3 |
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10.1109/CEC.2010.5586557 |
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Start Date
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18/07/2010 |
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End Date
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23/07/2010 |
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From page
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1 |
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To page
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8 |
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Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC-2010) |