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GECCO
2008
Springer

Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution

13 years 10 months ago
Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution
This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). This proposal computes a Gaussian model which approximates a Boltzmann distribution via the minimization of the Kullback Leibler divergence. The resulting approach needs only one parameter: the population size. A set of problems is presented to show advantages and comparative performance of this approach with state of the art continuous EDAs. Categories and Subject Descriptors: I.2[ARTIFICIAL INTELLIGENCE]Miscellaneous General Terms: Algorithms, Design, Experimentation, Performance, Theory.
Sergio Ivvan Valdez Peña, Arturo Hern&aacut
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Where GECCO
Authors Sergio Ivvan Valdez Peña, Arturo Hernández Aguirre, Salvador Botello Rionda
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