GA optimization of terminal antennas by the estimation of the population density of probability using dependency trees

Author

Nuñez, Francisco ; Skrivervik, Anja

Author_Institution

Lab. d´´Electromagn. et d´´Acoust., Ecole Polytech. Fed. de Lausanne, Switzerland

fYear

2003

fDate

1-3 Oct. 2003

Firstpage

336

Lastpage

339

Abstract

This paper presents an application of Bayesian network density of probability estimators to antenna optimisations, that involves relatively big number of parameters, multiple solutions, or local maxima, that increase the likelihood to converge towards not satisfactory suboptimal solutions. Three methods are compared for the case of a triband antenna optimization: dependency tree method (TREE), GA with linkage crossover operator (GLINX) and a simple GA with dual population diversity method (DUAL). Basic theory, the definition of the chosen structure to be optimized, simulation results and convergence curves are presented.

Keywords

belief networks; diversity reception; multifrequency antennas; optimisation; probability; Bayesian network; DUAL; GA linkage crossover operator; GLINX; TREE; dependency tree method; dual population diversity method; probability density estimators; triband antenna optimization; Approximation algorithms; Bayesian methods; Biological cells; Convergence; Couplings; Decision trees; Distributed computing; Electromagnets; Genetic mutations; Optimization methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Applied Electromagnetics and Communications, 2003. ICECom 2003. 17th International Conference on

Print_ISBN

953-6037-39-4

Type

conf

DOI

10.1109/ICECOM.2003.1291022

Filename

1291022