Adequate Variance Maintenance in a Normal EDA via the Potential-Selection Method

  • P. S. Ivvan Valdez
  • Arturo Hernández-Aguirre
  • Salvador Botello
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)


It is a known issue that Estimation of Distribution Algorithms (EDAs) tend to converge prematurely. There exist various articles which report that they are incapable of determining the adequate variance, getting trapped even in regions which does not contain any global neither local minimum. In this vein, several proposals intend to insert and to preserve diversity. The proposal range is from modifying the probability distribution by inserting artificial variance, to mutating individuals or the search distribution. This work presents a novel selection method which is used to estimate a weighted covariance matrix equipped with adequate magnitude and directions. The covariance matrix is directly related with the information acquired from the current population, hence no artificial artifacts are inserted. By comparing with state of the art EDAs using test problems and graphical and statistical measures, we evidence that our proposal avoids premature convergence, and solves difficult problems -in the sense of variance preservation-, without the need of complex models, mutation, or explicit variance scaling.


Covariance Matrix Evolutionary Computation Premature Convergence Distribution Algorithm Promising Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • P. S. Ivvan Valdez
    • 1
  • Arturo Hernández-Aguirre
    • 1
  • Salvador Botello
    • 1
  1. 1.Centre or Research in Mathematics (CIMAT)Guanajuato, Gto.México

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