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• W1829558465 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." @default.
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• W1829558465 date "2000-06-30" @default.
• W1829558465 modified "2023-09-28" @default.
• W1829558465 title "Combinatorial optimization by learning and simulation of Bayesian networks" @default.
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