Multiagent geographical models integrate very large numbers of spatial interactions. In order to validate these models a large amount of computing is necessary for their simulation and calibration. Here a new data-processing chain, including an automated calibration procedure, is tested on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimize three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. Thus the model is a useful tool for further multiple applications in empirical historical situations.
Archaeomedes (Ed.), 1998Des oppida aux métropoles (Anthropos, Paris)
2.
ArthurW B, 2009The Nature of Technology: What It Is and How It Evolves (Free Press, New York)
3.
BairochP, 1985De Jéricho à Mexico: Villes et économie dans l'histoire (Gallimard, Paris)
4.
BalciO, 1998, “Verification, validation, and testing”, in Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice Ed. BanksJ, (John Wiley, New York) pp 335–393
5.
BattyM, 2008, “Fifty years of urban modelling: Macro-statics to macro-dynamics”, in The Dynamics of Complex Urban Systems Eds AlbeverioAAndreySGiordanoDVancheriP, (Springer, Berlin) pp 1–21
6.
BerryB J L, 1964, “Cities as systems within systems of cities”Papers in Regional Science13147–163
7.
BourginePChavalariasDPerrierE, (Eds) 2009, “French roadmap for complex systems 2008–2009”, http://arxiv.org/abs/0907.2221
8.
BretagnolleAPumainD, 2010, “Simulating urban networks through multiscalar space-time dynamics (Europe and the United States, 17th-20th centuries)”Urban Studies472819–2839
9.
BretagnolleADaudéEPumainD, 2006, “From theory to modelling: Urban systems as complex systems”Cybergeo number 335, http://cybergeo.eu/index2420.html
10.
BuraSGuerin-PaceFMathianHPumainDSandersL, 1996, “Multiagent systems and the dynamics of a settlement system”Geographical Analysis28161–178
11.
CalvezBHutzlerG, 2006, “Automatic tuning of agent-based models using genetic algorithms”, in Multi-agent-based simulation VI (Springer, Berlin) pp 41–57
12.
CastiglioneFPappalardoFBernaschiMMottaS, 2007, “Optimization of HAART with genetic algorithms and agent-based models of HIV infection”Bioinformatics233350–3355
13.
ChristallerW, 1933Die Zentralen Orte in Süddeutschland. Eine Ökonomisch-Geographische Untersuchung über die Gesetzmässigkeit der Verbreitung und Entwicklung der Siedlungen mit Städtischen Funktionen (Fischer, Jena)
14.
CrooksACastleCBattyM, 2008, “Key challenges in agent-based modelling for geo-spatial simulation”Computers, Environment and Urban Systems32417–430
15.
DebKAgrawalSPratapAMeyarivanT, 2000, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, in Lecture Notes in Computer Science 1917, 849–858
16.
DiamondJ, 1997Guns, Germs, and Steel: The Fates of Human Societies (W W Norton, New York)
17.
DubozRVersmisseDTraversMRamatEShinY-J, 2010, “Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model”Ecological Modelling221840–849
18.
EmmerichMBeumeNNaujoksB, 2005, “An EMO algorithm using the hypervolume measure as selection criterion”, in Evolutionary Multi-criterion Optimization Eds ZitzlerEDebKThieleL, (Springer, Berlin) pp 62–76
FonsecaCPaqueteLLópez-IbánezM, 2006, “An improved dimension-sweep algorithm for the hypervolume indicator”, Proceedings of the 2006 Congress on Evolutionary Computation (IEEE Press, Piscataway, NJ) pp 1157–1163
HeppenstallA JEvansA JBirkinM H, (Eds), 2007, “Genetic algorithm optimisation of an agent-based model for simulating a retail market”Environment and Planning B: Planning and Design341051–1070
25.
HeppenstallA JCrooksA TSeeL MBattyM, (Eds), 2011Agent-based Models of Geographical Systems (Springer, Berlin)
26.
HinterdingR, 1995, “Gaussian mutation and self-adaption for numeric genetic algorithms”, in Proceedings of the IEEE International Conference on Evolutionary Computation, volume 1 (IEEE Press, Piscataway, NJ) pp 384–389
27.
JohnsonG A, 1977, “Aspects of regional analysis in archaeology”Annual Review of Anthropology6479–508
28.
LaneDPumainDvan der LeeuwSWestG, (Eds), 2009Complexity Perspectives in Innovation and Social Change (Springer, Berlin)
29.
LiuL, 1996, “Settlement patterns, chiefdom variability, and the development of early states in North China”Journal of Anthropological Archaeology15237–288
30.
MarcusJSabloffJ A, (Eds), 2008The Ancient City: New Perspectives on Urbanism in the Old and New World (School for Advanced Research Press, Santa Fe, NM)
31.
NaujoksZBeumeNEmmerichM, 2005“Multi-objective optimisation using S-metric selection: Application to three-dimensional solution spaces”Proceedings of the 2005 Congress on Evolutionary Computation21282–1289
32.
PennisiE, 2010, “Conquering by copying”Science328165–167
33.
PietroA DWhileLBaroneL, 2004, “Applying evolutionary algorithms to problems with noisy, time-consuming fitness functions”, Proceedings of the 2004 Congress on Evolutionary Computation21254–1261
34.
PumainD, 2009, “The evolution of city systems, between history and dynamics”International Journal of Environmental Creation121–12
35.
PumainD, 2011, “Multi-agent system modelling for urban systems: The series of SIMPOP models”, in Agent-based Models of Geographical Systems Eds HeppenstallA JCrooksA TSeeL MBattyM, (Springer, Berlin) pp 721–738
36.
PumainDSandersL, 2013, “Theoretical principles in inter-urban simulation models: A comparison”Environment and Planning A452243–2260
37.
PumainDSandersLBretagnolleAGlisseBMathianH, 2009, “The future of urban systems”, in Complexity Perspectives on Innovation and Social Change Eds LaneDPumainDvan der LeeuwSWestG, (Springer, Berlin) pp 331–359
38.
RenfrewCPostonT, 1979, “Discontinuities in the endogenous change of settlement pattern”, in Transformations: Mathematical Approaches to Culture Change Eds RenfrewCCookeK L, (Academic Press, New York) pp 437–461
39.
ReuillonRChuffartFLeclaireMFaureTDumoulinNHillD, 2010, “Declarative task delegation in OpenMOLE”, Proceedings of the 2010 International Conference on High Performance Computing and Simulation55–62
40.
ReuillonRLeclaireMRey-CoyrehourcqS, 2013, “OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models”Future Generation Computer Systems291981–1990
41.
Rey-CoyrehourcqS, 2014, Simprocess, an Integrated Method to Validate Agent-based Models in Geography PhD thesis in progress, Université Paris 1 Pantheon-Sorbonne
42.
RobsonB T, 1973Urban Growth, an Approach (Methuen, London)
43.
SandersL, 2007, “Objets géographiques et simulation agent, entre thématique et méthodologie”Revue Internationale de Géomatique17135–160
44.
SandersL, 2012, “Regards croisés de géographes, économistes et archéologues sur la hiérarchie des systèmes de peuplement: De l'empirie aux systèmes complexes”Région et Développement36127–146
45.
SandersLPumainDMathianHGuérin-PaceFBuraS, 1997, “SIMPOP: A multi-agent system for the study of urbanism”Environment and Planning B: Planning and Design24287–305
46.
SargentR G, 2005, “Verification and validation of simulation models”, in Proceedings of the 37th Conference on Winter Simulation WSC2005, Washington, DC, pp 130–143
47.
SchmittC, 2014Modeling Settlement Systems Dynamics: From SimpopLocal to SimpopNet PhD thesis, Université Paris 1 Pantheon-Sorbonne, France
48.
SharmaVSwayneDLamDSchertzerW, 2006, “Auto-calibration of hydrological models using high performance computing”Proceedings of the 3rd Biennial meeting of the International Environmental Modelling and Software Society, http://www.iemss.org/iemss2006/papers/s%/202_sharma_02.pdf
49.
SimonH, 1955, “On a class of skewed distribution functions”Biometrika42425–440
50.
SolomatineD PDibikeY BKukuricN, 1999“Automatic calibration of groundwater models using global optimization techniques”Hydrological Sciences Journal44879–894
StonedahlFWilenskyW U, 2010a, “Evolving viral marketing strategies”, in Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (ACM, New York) pp 1195–1202
53.
StonedahlFWilenskyU, 2010b, “Evolutionary robustness checking in the artificial Anasazi model”, in Proceedings of the AAAI Fall Symposium on Complex Adaptive Systems: Resilience, Robustness, and Evolvability (AAAI Press, Menio Park, CA) pp 120–129
54.
TurchinP, 2003Historical Dynamics: Why States Rise and Fall (Princeton University Press, Princeton, NJ)
55.
VerhulstP F, 1845, “Recherches mathématiques sur la loi d'accroissement de la population”Nouveaux Mémoires de l'Academie Royale des Sciences et Belles-Lettres de Bruxelles181–41
56.
WhitleyDRanaSHeckendornR, 1997, “Island model genetic algorithms and linearly separable problems”, in Evolutionary Computing: AISB International Workshop, Selected Papers Eds CorneDShepiroJ L, (Springer, Berlin) pp 109–125
57.
WilsonA G, 1971, “A family of spatial interaction models, and associated developments”, Environment and Planning A31–32
58.
ZitzlerEThieleL, 1998, “Multiobjective optimization using evolutionary algorithms—a comparative case study”, in Proceeding of the 5th International Conference on Parallel Problem Solving from Nature-PPSN V Eds EibenA EBöckTSchoenauerMSchwefelH-P, (Springer, Berlin) pp 292–301
