A gene expression programming algorithm for multiobjective site-search problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8187

@InProceedings{Liu:2010:ICNC,

• author = "Mengwei Liu and Xia Li and Tao Liu and Dan Li and Zheng Lin",
• title = "A gene expression programming algorithm for multiobjective site-search problem",
• booktitle = "Sixth International Conference on Natural Computation (ICNC 2010)",
• year = "2010",
• month = "10-12 " # aug,
• volume = "1",
• pages = "14--18",
• 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)",
• DOI = "doi:10.1109/ICNC.2010.5582975",
• 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