Robust Evolutionary Algorithm Design for Socio-economic Simulation

• Published in: Computational economics Vol. 28; no. 4; pp. 355 - 370
• Main Authors: Amman, Hans M, Poutré, Han, Alkemade, Floortje
• Format: Journal Article
• Language: English
• Published: Dordrecht Society for Computational Economics 01.11.2006; Springer Nature B.V
• Series: Computational Economics
• Subjects: Algorithms; Chromosomes; Computational methods; Computer science; Economic models; Economic theory; Economics; Equilibrium; evolutionary algorithms; Evolutionary economics; Genetic algorithms; Mutation; Population; Simulation; Socioeconomic development; Socioeconomic factors; Studies
• ISSN: 0927-7099; 1572-9974
• 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. Copyright Springer 2006