Automatic Tuning of Agent-Based Models Using Genetic Algorithms

  • Benoît Calvez
  • Guillaume Hutzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3891)


When developing multi-agent systems (MAS) or models in the context of agent-based simulation (ABS), the tuning of the model constitutes a crucial step of the design process. Indeed, agent-based models are generally characterized by lots of parameters, which together determine the global dynamics of the system. Moreover, small changes made to a single parameter sometimes lead to a radical modification of the dynamics of the whole system. The development and the parameter setting of an agent-based model can thus become long and tedious if we have no accurate, automatic and systematic strategy to explore this parameter space.

That’s the development of such a strategy that we work on suggesting the use of genetic algorithms. The idea is to capture in the fitness function the goal of the design process (efficiency for MAS that realize a given function, realism for agent-based models, etc.) and to make the model automatically evolve in that direction. However the use of genetic algorithms (GA) in the context of ABS brings specific difficulties that we develop in this article, explaining possible solutions and illustrating them on a simple and well-known model: the food-foraging by a colony of ants.


Genetic Algorithm Parameter Space Evaporation Rate Emergent Phenomenon Good Chromosome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Sallans, B., Pfister, A., Karatzoglou, A., Dorffner, G.: Simulation and validation of an integrated markets model. J. Artificial Societies and Social Simulation 6(4) (2003)Google Scholar
  2. 2.
    Tisue, S., Wilensky, U.: Netlogo: Design and Implementation of a Multi-Agent Modeling Environment. Proceedings of Agent 2004 (2004)Google Scholar
  3. 3.
    Brueckner, S., Parunak, H.V.D.: Resource-Aware Exploration of the Emergent Dynamics of Simulated Systems. AAMAS 2003, 781–788 (2003)Google Scholar
  4. 4.
    Fehler, M., Klügl, F., Puppe, F.: Techniques for analysis and calibration of multi-agent simulations. In: Gleizes, M.-P., Omicini, A., Zambonelli, F. (eds.) ESAW 2004. LNCS (LNAI), vol. 3451, pp. 305–321. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing Journal (2003)Google Scholar
  6. 6.
    Efficient parallel genetic algorithms: theory and practice. Computer Methods in Applied Mechanics and Engineering 186 (2000)Google Scholar
  7. 7.
    Jin, Y., Olhofer, M., Sendhoff, B.: A Framework for Evolutionary Optimization with Approximate Fitness Functions. IEEE Transactions on Evolutionary Computation 6(5), 481–494 (2002)CrossRefGoogle Scholar
  8. 8.
    Beyer, H.G.: Evolutionary Algorithms in Noisy Environments: Theoretical Issues and Guidelines for Practice. Computer methods in applied mechanics and engineering 186 (2000)Google Scholar
  9. 9.
    Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and the sizing of populations. Complex System 6 (1992)Google Scholar
  10. 10.
    Beker, T., Hadany, L.: Noise and elitism in evolutionary computation. In: Soft Computing Systems - Design, Management and Applications, pp. 193–203 (2002)Google Scholar
  11. 11.
    Amar, P., Bernot, G., Norris, V.: Modelling and Simulation of Large Assemblies of Proteins. Proceedings of the Dieppe spring school on Modelling and simulation of biological processes in the context of genomics, 36–42 (2002)Google Scholar
  12. 12.
    Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. In: Proceedings of the IEEE, vol. 89, pp. 1275–1296. IEEE Press, Los Alamitos (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Benoît Calvez
    • 1
  • Guillaume Hutzler
    • 1
  1. 1.Universite d’Evry-Val d’Essonne/CNRS, LaMI, UMR 8042EvryFrance

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