Robust evolutionary algorithm design for socio-economic simulation
| Autor: | Hans M. Amman, Floortje Alkemade, Han La Poutré |
|---|---|
| Přispěvatelé: | Intelligent and autonomous systems, Information Systems IE&IS, Innovation Technology Entrepr. & Marketing |
| Jazyk: | angličtina |
| Rok vydání: | 2006 |
| Předmět: |
Computational economics
business.industry Computer science Economics Econometrics and Finance (miscellaneous) Evolutionary algorithm Interactive evolutionary computation Machine learning computer.software_genre Computer Science Applications Human-based evolutionary computation Economic model Artificial intelligence business Robustness (economics) computer Evolutionary programming Economic problem |
| Zdroj: | Computational Economics, 28, 355-370 Computational Economics, 28(4), 355-370. Springer |
| ISSN: | 0927-7099 1572-9974 |
| DOI: | 10.1007/s10614-006-9051-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink