A gene expression programming algorithm for multiobjective site-search problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7906
- @InProceedings{Liu:2010:ICNC,
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author = "Mengwei Liu and Xia Li and Tao Liu and Dan Li and
Zheng Lin",
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title = "A gene expression programming algorithm for
multiobjective site-search problem",
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booktitle = "Sixth International Conference on Natural Computation
(ICNC 2010)",
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year = "2010",
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month = "10-12 " # aug,
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volume = "1",
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pages = "14--18",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, bohachevsky function, MOP2
function, pareto-front, shubert function, expression
trees, geographical information system, linear coding
method, multiobjective site-search problem, simple
strings coding strategy, spatial analysis problem,
pareto optimisation, geographic information systems,
trees (mathematics)",
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DOI = "doi:10.1109/ICNC.2010.5582975",
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abstract = "Multiobjective site selection is a class complicated
spatial analysis problem which can hardly be solved
with traditional methods of Geographical Information
System (GIS). In this paper we described an approach
based on the gene expression programming (GEP)
algorithm, with which the multiobjective site-search
problems can be resolved. The validity of this method
is verified by using MOP2 function, Bohachevsky
function and Shubert function. By the comparison with
genetic algorithms, it is concluded that the proposed
GEP method using the expression trees/simple strings
coding strategy can generate more approximate
Pareto-front than the GAs using the linear coding
method. This proposed model is finally applied to
facilities optimal location search in Guangzhou.",
-
notes = "Also known as \cite{5582975}",
- }
Genetic Programming entries for
Mengwei Liu
Xia Li
Tao Liu
Dan Li
Zheng Lin
Citations