Computational Economics

, Volume 28, Issue 4, pp 355–370

Robust Evolutionary Algorithm Design for Socio-economic Simulation

  • Floortje Alkemade
  • Han La Poutré
  • Hans M. Amman

DOI: 10.1007/s10614-006-9051-5

Cite this article as:
Alkemade, F., Poutré, H.L. & Amman, H.M. Comput Econ (2006) 28: 355. doi:10.1007/s10614-006-9051-5


Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.


evolutionary algorithmssimulation

Copyright information

© Springer 2006

Authors and Affiliations

  • Floortje Alkemade
    • 1
  • Han La Poutré
    • 2
    • 3
  • Hans M. Amman
    • 4
  1. 1.Department of Innovation StudiesUtrecht UniversityUtrechtThe Netherlands
  2. 2.Centre for Computer Science and Mathematics (CWI)AmsterdamThe Netherlands
  3. 3.Eindhoven University of Technology (TU/e)EindhovenThe Netherlands
  4. 4.Utrecht UniversityUtrechtThe Netherlands