Optimal spatial search using genetic algorithms and GIS

This study demonstrates that genetic algorithms are capable of producing satisfying results for optimal spatial search under complex situations. We successfully solve a spatial search problem using the proposed method to allocate the facility according to the population constraint from GIS. The search algorithm is very simple using the mechanics of natural selection in biology. The proposed method can be used as a planning tool that can help urban planners to improve development efficiency in site selection. The method is developed by a common computer language which can directly use the full functions of a commercial GA package through the DLL and can import the spatial data from GIS. This integration is useful for solving realistic problems by using large spatial data sets. The programming can be easily adapted to other applications by just modifying the fitness functions instead of changing the model itself. The proposed method has been tested in the city of Hong Kong, a densely populated region. The population data are obtained from the census department and the population density is prepared in GIS as the main inputs to the GA programming.