  This paper shows how the Bayesian network paradigm can be used in order to
solve combinatorial optimization problems. To do it some methods of structure
learning from data and simulation of Bayesian networks are inserted inside
Estimation of Distribution Algorithms (EDA). EDA are a new tool for
evolutionary computation in which populations of individuals are created by
estimation and simulation of the joint probability distribution of the selected
individuals. We propose new approaches to EDA for combinatorial optimization
based on the theory of probabilistic graphical models. Experimental results are
also presented.
