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Multi-agent geographical models integrate very large numbers of spatialinteractions. In order to validate those models large amount of computing isnecessary for their simulation and calibration. Here a new data processingchain including an automated calibration procedure is experimented on acomputational grid using evolutionary algorithms. This is applied for the firsttime to a geographical model designed to simulate the evolution of an earlyurban settlement system. The method enables us to reduce the computing time andprovides robust results. Using this method, we identify several parametersettings that minimise three objective functions that quantify how closely themodel results match a reference pattern. As the values of each parameter indifferent settings are very close, this estimation considerably reduces theinitial possible domain of variation of the parameters. The model is thus auseful tool for further multiple applications on empirical historicalsituations.

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model