A gene expression programming algorithm for multiobjective site-search problem

  • Mengwei Liu*
  • , Xia Li
  • , Tao Liu
  • , Dan Li
  • , Lin Zheng
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2010 6th International Conference on Natural Computation, ICNC 2010
Pages14-18
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 6th International Conference on Natural Computation, ICNC'10 - Yantai, Shandong, China
Duration: 10 Aug 201012 Aug 2010

Publication series

NameProceedings - 2010 6th International Conference on Natural Computation, ICNC 2010
Volume1

Conference

Conference2010 6th International Conference on Natural Computation, ICNC'10
Country/TerritoryChina
CityYantai, Shandong
Period10/08/1012/08/10

Keywords

  • Evolutionary algorithm
  • GIS
  • Gene expression programming
  • Multi-objective optimization
  • Site selection

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