Combinatorial Optimization by Learning and Simulation of Bayesian Networks

This paper shows how the Bayesian network paradigm can be used in order to solve com­ binatorial optimization problems. To do it some methods of structure learning from data and simulation of Bayesian networks are in­ serted inside Estimation of Distribution Al­ gorithms (EDA). EDA are a new tool for evo­ lutionary computation in which populations of individuals are created by estimation and simulation of the joint probability distribu­ tion of the selected individuals. We propose new approaches to EDA for combinatorial op­ timization based on the theory of probabilis­ tic graphical models. Experimental results are also presented.