Natural Computing

, Volume 15, Issue 2, pp 335–354 | Cite as

On the model updating operators in univariate estimation of distribution algorithms



The role of the selection operation—that stochastically discriminate between individuals based on their merit—on the updating of the probability model in univariate estimation of distribution algorithms is investigated. Necessary conditions for an operator to model selection in such a way that it can be used directly for updating the probability model are postulated. A family of such operators that generalize current model updating mechanisms is proposed. A thorough theoretical analysis of these operators is presented, including a study on operator equivalence. A comprehensive set of examples is provided aiming at illustrating key concepts, main results, and their relevance.


Compact genetic algorithm Estimation of distribution algorithms Selection operator Theoretical analysis 



Andrey Bronevich is grateful to the Erasmus Mundus Triple I Consortium that supported a 10-months academic visit to the University of Algarve in 2010. This work is an outcome of a research cooperation between the authors that began with this visit. Andrey Bronevich also thanks the National Research University Higher School of Economics, Moscow, Russia for providing him with 1 month research grant for visiting University of Algarve in July 2014 facilitating the conclusion of the work. José Valente de Oliveira also thanks the National Research University Higher School of Economics, for inviting him for one week visit in November 2014.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.CEOT and FCTUniversidade do AlgarveFaroPortugal

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