Optimization by learning and simulation of Bayesian and Gaussian networks

Optimization by learning and simulation of Bayesian and Gaussian networks (1999)

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by P. Larrañaga , R. Etxeberria , J. A. Lozano , J.M. Peña , J. M. Pe~na

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Datum	Value	Source
TITLE	Optimization by learning and simulation of Bayesian and Gaussian networks	user correction - Legacy Corrections
AUTHOR NAME	P. Larrañaga	user correction - Legacy Corrections
AUTHOR NAME	R. Etxeberria	user correction - Legacy Corrections
AUTHOR AFFIL	Intelligent Systems Group; Dept. of Computer Science and Articial Intelligence; University of the Basque Country	user correction - Legacy Corrections
AUTHOR NAME	J. A. Lozano	user correction - Legacy Corrections
AUTHOR AFFIL	Intelligent Systems Group; Dept. of Computer Science and Articial Intelligence; University of the Basque Country	user correction - Legacy Corrections
AUTHOR NAME	J.M. Peña	user correction - Legacy Corrections
AUTHOR NAME	J. M. Pe~na	user correction - Legacy Corrections
AUTHOR AFFIL	Intelligent Systems Group; Dept. of Computer Science and Articial Intelligence; University of the Basque Country	user correction - Legacy Corrections
ABSTRACT	Estimation of Distribution Algorithms (EDA) constitute an example of stochastics heuristics based on populations of individuals every of which encode the possible solutions to the optimization problem. These populations of individuals evolve in succesive generations as the search progresses -- organized in the same way as most evolutionary computation heuristics. In opposition to most evolutionary computation paradigms which consider the crossing and mutation operators as essential tools to generate new populations, EDA replaces those operators by the estimation and simulation of the joint probability distribution of the selected individuals. In this work, after making a review of the different approaches based on EDA for problems of combinatorial optimization as well as for problems of optimization in continuous domains, we propose new approaches based on the theory of probabilistic graphical models to solve problems in both domains. More precisely, we propose to adapt algorit...	user correction - Legacy Corrections
YEAR	1999	user correction - Legacy Corrections
CITATIONS	95 found	ParsCit 1.0