A Matlab Genetic Programming Approach to Topographic Mesh Surface Generation
Created by W.Langdon from
gpbibliography.bib Revision:1.7852
 @InCollection{Rodriguez:2011:intech,

author = "Katya Rodriguez V. and Rosalva Mendoza R.",

title = "A Matlab Genetic Programming Approach to Topographic
Mesh Surface Generation",

booktitle = "Engineering Education and Research Using MATLAB",

publisher = "INTECH Open Access Publisher",

year = "2011",

editor = "Ali H. Assi",

chapter = "18",

month = oct # "~10",

keywords = "genetic algorithms, genetic programming",

isbn13 = "9789533076560",

URL = "http://www.intechopen.com/articles/show/title/amatlabgeneticprogrammingapproachtotopographicmeshsurfacegeneration",

URL = "http://www.intechopen.com/download/pdf/pdfs_id/21403",

URL = "http://www.intechopen.com/books/engineeringeducationandresearchusingmatlab",

DOI = "doi:10.5772/22376",

size = "16 pages",

language = "eng",

oai = "oai:intechopen.com:21403",

bibsource = "OAIPMH server at www.intechopen.com",

abstract = "The problem of surface approximation by means of soft
mathematical functions is a relevant topic in
Hydrology. The generation of these functions allows
solving implicitly some of the most important
calculation in order to predict the behaviour of the
hydrological basin. Thus, this work proposes the use of
an Evolutionary Algorithm (EA) (Baeck, 1996) to
generate 3D mesh surface from a set of topographic
data. In literature, there are only few existing works
about the use of Evolutionary Algorithms (EAs) applied
to the reconstruction of topographic surfaces, most of
them are based on Genetic Algorithms (GAs) (Holland,
1975; Goldberg, 1989) as an approximation polynomial
parameter estimator. Thus, this paper introduces a
Genetic Programming (GP) approach whose aim is to
obtain a mathematical function that allows a compact
representation of the surface of the topographic
information. This surface generation problem is then
formulated as symbolic regression. The use of EAs,
specifically GP (Koza, 1990; Banzhaf et al., 1998),
constitute a promise alternative for the traditional
interpolation techniques that employ approximation
polynomials, due to GP integrates in a natural way the
common nonlinearities present in complex interpolation
problems. This proposal is then applied to a set of
topographic data corresponding to the Mezcalapa River
zone, which is the local name of the Grijalva River
located at the southeast of the Mexican Republic and it
is one of the most important rivers due to its flow and
generation of electric energy.
The GP algorithm is programmed in MATLAB and the
results produced by means of this GP approach give
indication of a significant improvement in terms of the
quality of the approximation in relation to the results
obtained by means of approximation polynomials method
applied to this region. In the following section a
brief review of some works on mathematical modelling
applied to Civil and Hydraulic Engineering are
detailed. After that, description of genetic
programming algorithm and its implementation in MATLAB
are presented. The application of this evolutionary
method to evolve mathematical models in order to
construct topographic surface is presented. Finally
results and conclusions are drawn.",

notes = "Published: October 10, 2011 under CC BY 3.0 license",
 }
Genetic Programming entries for
Katya RodriguezVazquez
Rosalva Mendoza Ramrez
Citations