A gene expression programming algorithm for multiobjective site-search problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  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, genetic algorithms, 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