## Combinatorial optimization by learning and simulation of Bayesian networks (2000)

Venue: | in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence |

Citations: | 21 - 10 self |

### BibTeX

@INPROCEEDINGS{Larrañaga00combinatorialoptimization,

author = {Pedro Larrañaga and Ramon Etxeberria and José A. Lozano and José M. Peña},

title = {Combinatorial optimization by learning and simulation of Bayesian networks},

booktitle = {in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence},

year = {2000},

pages = {343--352},

publisher = {Morgan Kaufmann}

}

### Years of Citing Articles

### OpenURL

### Abstract

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.