Una extensión del algoritmo MIMIC mediante Cópulas

Rogelio Salinas Gutiérrez, Arturo Hernández Aguirre, Enrique Raúl Villa Diharce


A new way of modeling probabilistic dependencies in Mutual Information Maximization for Input Clustering (MIMIC) algorithm is presented. By means of copulas it is possible to separate the dependence structure from marginal distributions in a joint distribution. The use of copulas as a mechanism for modeling distributions and its applicaction to MIMIC is illustrated on Rosenbrock test function.


Global optimization; Evolutionary computing; Estimation of Distribution Algorithms; Copulas


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DOI: https://doi.org/10.21640/ns.v2i3.218


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