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.

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Sergio Ivvan Valdez Peña, Arturo Hernández Aguirre, Salvador Botello Rionda
• Keywords: Distribution Algorithm, Edas Called Elitist, GECCO 2008, Marginal Distribution Algorithm, Optimization