Using Genetic Algorithms for Model Selection in Graphical Models

Model selection in graphical models is still not fully
investigated. The main difficulty lies in the search space of all
possible models which grows more than exponentially with the number
of variables involved. Here, genetic algorithms seem to be a
reasonable strategy to find good fitting models for a given data
set. In this paper, we adapt them to the problem of model search in
graphical models and discuss their performance by conducting
simulation studies.