By Steve Carver
This article displays the interdisciplinary nature of GIS learn and comprises insurance of such topics as: digital GIS; spatial research; synthetic intelligence; spatial brokers and fuzzy platforms; and space-time GIS and GIS purposes.
Read or Download Innovations In GIS 5: Selected Papers From The Fifth National Conference On GIS Research UK PDF
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Additional info for Innovations In GIS 5: Selected Papers From The Fifth National Conference On GIS Research UK
A cross-over operator on the level of populations is implicit: it occurs when objects in mating populations exchange genetic material. The cross-over of populations takes, as input, two populations and generates one offspring population. At the level of organisms the cross-over takes as an input two organisms selected in mating and produces one organism. A cross-over is chromosome-specific. ■ SEA has two types of mutation (for organisms and for populations): ‘Big M’ mutation and ‘small m’ mutation.
K. ), Three-Dimensional Modeling with Geoscientific Information Systems, London: Kluwer Academic Publishers. F. and LEVINGSTON, D. (1997) Spatio-Temporal Interpolation in Four Dimensional Coastal Process Models, GIS UK 97. C. (1985) Map Algebra, Cambridge, MA: Harvard University. , RYSTEDT, B. and GOULD, P. (1971) Multiple Location Analysis, Lund Studies in Geography, No. 12.
RAPER ■ Objective function in SEA is defined only for populations (as only the population fitness maps into the problem space) and is equivalent to the population’s fitness. It expresses the fitness of the evolutionary units with respect to the problem objectives. 3 SPATIAL EVOLUTIONARY ALGORITHM—IMPLEMENTATION Implementation aspects of SEA are discussed in this paper within the context of the Planar Covering Problem. It has to be stressed that SEA is a framework for the design of evolutionary models of spatial problems and, as such, can accommodate many different models on many levels of complexity.