Bayesian Network Structure learning using Cooperative Coevolution

Barrière, Olivier

Lutton, Evelyne

Wuillemin, Pierre-Henri
LIP6 - Pôle IA
Université Paris 6
8, rue du capitaine Scott F-75015 Paris, France
email: pierre-henri.wuillemin@lip6.fr   home: www-desir.lip6.fr/~phw

Barrière, Olivier and Lutton, Evelyne and Wuillemin, Pierre-Henri (2009) "Bayesian Network Structure learning using Cooperative Coevolution". In Genetic and Evolutionary Computation Conference 09, pp. ?--?.

We propose a cooperative-coevolution -- Parisian trend -- algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It is based on an intermediate stage which consists of evaluating an independence model of the data to be modelled. The Parisian cooperative coevolution is particularly well sui-ted to the structure of this intermediate problem, and allows to represent an independence model with help of a whole population, each individual being an independence statement, i.e. a component of the independence model. Once an independence model is estimated, a Bayesian network can be built. This two level resolution of the complex problem of Bayesian network structure estimation has the major advantage to avoid the difficult problem of direct acyclic graph representation within an evolutionary algorithm, which causes many troubles related to constraints handling and slows down algorithms. Comparative results with a deterministic algorithm, PC, on two test cases (including the Insurance BN benchmark), prove the efficiency of IMPEA, which provides better results than PC in a comparable computation time, and which is able to tackle more complex issues than PC.

@InProceedings{,
  author = {Barrière, Olivier and Lutton, Evelyne and Wuillemin, Pierre-Henri},
  title = {Bayesian Network Structure learning using Cooperative Coevolution},
  booktitle = {Genetic and Evolutionary Computation Conference 09},
  year = {2009},
  pages = {?--?}
}