A GEP-based spatial decision support system for multisite land use allocation

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  author =       "Khalid Eldrandaly",
  title =        "A GEP-based spatial decision support system for
                 multisite land use allocation",
  journal =      "Applied Soft Computing",
  year =         "2009",
  volume =       "10",
  number =       "3",
  pages =        "694--702",
  month =        jun,
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2009.07.014",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-4X2DCVV-2/2/c8addfbfae7f3e5035dc45213f378416",
  keywords =     "genetic algorithms, genetic programming, Spatial
                 decision support systems, Multisite land use
                 allocation, GIS, Gene expression programming",
  abstract =     "Multisite Land Use Allocation Problem (MLUA) refers 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. To cope
                 with this type of problems, intelligent techniques such
                 as genetic algorithms, and simulated annealing, have
                 been used. Research in the area of Spatial Decision
                 Support Systems (SDSS) for resource allocation issues,
                 a new scientific area of information system
                 applications developed to support semi-structured or
                 unstructured spatial decisions, has recently generated
                 attention for integrating Artificial Intelligence (AI)
                 techniques with Geographic Information Systems (GIS).
                 In this paper we demonstrate how GIS can be integrated
                 with Gene Expression Programming (GEP), a recently
                 developed AI approach, for solving MLUA problems. The
                 feasibility of the proposed approach in solving MLUA
                 problems was checked using a fictive case study. The
                 results indicated that the proposed approach gives good
                 and satisfactory results.",
  notes =        "King Abdulaziz University, P.O. Box 80105, Jeddah
                 21589, Saudi Arabia",

Genetic Programming entries for Khalid Aly Eldrandaly