Integrating Gene Expression Programming and Geographic Information Systems for Solving a Multi Site Land Use Allocation Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Eldrandaly:2009:AJAS,
  title =        "Integrating Gene Expression Programming and Geographic
                 Information Systems for Solving a Multi Site Land Use
                 Allocation Problem",
  author =       "Khalid A. Eldrandaly",
  journal =      "American Journal of Applied Sciences",
  year =         "2009",
  volume =       "6",
  number =       "5",
  pages =        "1021--1027",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Multi site land use allocation,
                 GIS, SDSS",
  publisher =    "Science Publications",
  ISSN =         "1546-9239",
  URL =          "http://www.scipub.org/fulltext/ajas/ajas651021-1027.pdf",
  URL =          "http://thescipub.com/html/10.3844/ajassp.2009.1021.1027",
  broken =       "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=15469239\&date=2009\&volume=6\&issue=5\&spage=1021",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:374b808b659956eb2527109ade485337",
  DOI =          "doi:10.3844/ajassp.2009.1021.1027",
  size =         "7 pages",
  abstract =     "Problem statement: Land use planning may be defined as
                 the process of allocating different activities or uses
                 to specific units of area within a region. Multi sites
                 Land Use Allocation Problems (MLUA) refer to the
                 problem of allocating more than one land use type in an
                 area. MLUA problem is one of the truly NP Complete
                 (combinatorial optimisation) problems. Approach: To
                 cope with this type of problems, intelligent techniques
                 such as genetic algorithms and simulated annealing,
                 have been used. In this study a new approach for
                 solving MLUA problems was proposed by integrating Gene
                 Expression Programming (GEP) and GIS. The feasibility
                 of the proposed approach in solving MLUA problems was
                 checked using a fictive case study. Results: The
                 results indicated clearly that the proposed approach
                 gives good and satisfactory results.
                 Conclusion/Recommendation: Integrating GIS and GEP is a
                 promising and efficient approach for solving MLUA
                 problems. This research focused on minimising the
                 development costs and maximising the compactness of the
                 allocated land use. The optimization model can be
                 extended in the future to maximize also the spatial
                 contiguity of the allocated land use.",
  notes =        "Faculty of Computers and Informatics, Zagazig
                 University, Egypt",
}

Genetic Programming entries for Khalid Aly Eldrandaly

Citations