Optimization in continuous domains by learning and simulation of Gaussian networks
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BibTeX
@MISC{Larrañaga_optimizationin,
author = {Pedro Larrañaga and Ramon Etxeberria and José A. Lozano and José M. Peña},
title = {Optimization in continuous domains by learning and simulation of Gaussian networks},
year = {}
}
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Abstract
This paper shows how the Gaussian network paradigm can be used to solve optimization problems in continuous domains. Some methods of structure learning from data and simulation of Gaussian networks are applied in the Estimation of Distribution Algorithm (EDA) as well as new methods based on information theory are proposed. Experimental results are also presented. 1 Estimation of Distribution Algorithms approaches in continuous domains Figure 1 shows a schematic of the EDA approach for continuous domains. We will use x = (x 1 ; : : : ; xn ) to denote individuals, and D l to denote the population of N individuals in the l-th generation. Similarly, D Se l will represent the population of the selected Se individuals from D l . In the EDA [9] our interest will be to estimate f(x j D Se ), that is, the joint probability density function over one individual x being among the selected individuals. We denote as f l (x) = f l (x j D Se l 1 ) the joint density of the l-th genera...
