Bayesian network structure learning using cooperative coevolution

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 suited 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 algor...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Olivier Barrière, Evelyne Lutton, Pierre-Henri Wuillemin
• Keywords: Bayesian Network, GECCO 2009, Independence Model, Optimization, Structure Estimation