EDNA: Estimation of Dependency Networks Algorithm

  • Authors:

  • José A. Gámez;Juan L. Mateo;José M. Puerta

  • Affiliations:

  • Computing Systems Department, Intelligent Systems and Data Mining Group --- i3A, University of Castilla-La Mancha, Albacete, 02071, Spain;Computing Systems Department, Intelligent Systems and Data Mining Group --- i3A, University of Castilla-La Mancha, Albacete, 02071, Spain;Computing Systems Department, Intelligent Systems and Data Mining Group --- i3A, University of Castilla-La Mancha, Albacete, 02071, Spain

  • Venue:
  • IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this work we present a new proposal in order to model the probability distribution in the estimation of distribution algorithms. This approach is based on using dependency networks [1] instead of Bayesian networks or simpler models in which structure is limited. Dependency networks are probabilistic graphical models similar to Bayesian networks, but with a significant difference: they allow directed cycles in the graph. This difference can be an important advantage because of two main reasons. First, in some real problems cyclic relationships appear between variables an this fact cannot be represented in a Bayesian network. Secondly, dependency networks can be built easily due to the fact that there is no need to check the existence of cycles as in a Bayesian network.In this paper we propose to use a general (multivariate) model in order to deal with a richer representation, however, in this initial approach to the problem we also propose to constraint the construction phase in order to use only bivariate statistics. The algorithm is compared with classical approaches with the same complexity order, i.e. bivariate models as chains and trees.