Uncertainty in Artificial Intelligence
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Combinatorial Optimization by Learning and Simulation of Bayesian Networks
Pedro Larrañaga, Ramon Etxeberria, Jose Lozano, Jose Pena
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.
Keywords:
Pages: 343-352
PS Link: http://www.sc.ehu.es/ccwbayes/postscript/uai00.ps
PDF Link: /papers/00/p343-larranaga.pdf
BibTex:
@INPROCEEDINGS{Larrañaga00,
AUTHOR = "Pedro Larrañaga and Ramon Etxeberria and Jose Lozano and Jose Pena",
TITLE = "Combinatorial Optimization by Learning and Simulation of Bayesian Networks",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "343--352"
}


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