Computational Economics

, Volume 28, Issue 4, pp 355–370

Robust Evolutionary Algorithm Design for Socio-economic Simulation

Authors

    • Department of Innovation StudiesUtrecht University
  • Han La Poutré
    • Centre for Computer Science and Mathematics (CWI)
    • Eindhoven University of Technology (TU/e)
  • Hans M. Amman
    • Utrecht University
Article

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

Abstract

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.

Keywords

evolutionary algorithmssimulation

Copyright information

© Springer 2006