Título
Hierarchical Genetic Algorithm for B-Spline Surface Approximation of Smooth Explicit Data
Autor
CARLOS HUGO GARCIA CAPULIN
FRANCISCO JAVIER CUEVAS DE LA ROSA
GERARDO TREJO CABALLERO
Horacio Rostro Gonzalez
Nivel de Acceso
En Embargo
Resumen o descripción
B-spline surface approximation has been widely used in many applications such as CAD, medical imaging, reverse engineering, and
geometric modeling. Given a data set of measures, the surface approximation aims to find a surface that optimally fits the data set.
One of the main problems associated with surface approximation by B-splines is the adequate selection of the number and location
of the knots, as well as the solution of the system of equations generated by tensor product spline surfaces. In this work, we use a
hierarchical genetic algorithm (HGA) to tackle the B-spline surface approximation of smooth explicit data. The proposed approach
is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and
location of the knots for each surface dimension and the B-spline coefficients simultaneously.The method is fully based on genetic
algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to
validate the efficacy of the proposed approach, simulation results from several tests on smooth surfaces and comparison with a
successful method have been included.
Fecha de publicación
junio de 2014
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Repositorio Orígen
REPOSITORIO INSTITUCIONAL DEL CIO
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