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Evolutionäre Optimierungsverfahren und ihr Einsatz in der ökonomischen Forschung

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  • Clemens, Christiane
  • Riechmann, Thomas

Abstract

Dieser Beitrag bietet eine Einführung in eine Gruppe moderner Algorithmen, den sogenannten evolutionären Optimierungsverfahren. Anhand eines einfachen Beispiels wird die grundsätzliche Funktionsweise dieser Algorithmen skizziert. Darüberhinaus wird ein Überblick über die Anwendungsmöglichkeiten dieser Verfahren innerhalb der ökonomischen Forschung - insbesondere im Bereich der Modellierung von Lernprozessen - gegeben.

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File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-195.pdf
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Bibliographic Info

Paper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-195.

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Length: 24 pages
Date of creation: Jun 1996
Date of revision:
Handle: RePEc:han:dpaper:dp-195

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References

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  1. Arifovic, Jasmina & Eaton, Curtis, 1995. "Coordination via Genetic Learning," Computational Economics, Society for Computational Economics, vol. 8(3), pages 181-203, August.
  2. Tesfatsion, Leigh, 1995. "How to Get Alife," Staff General Research Papers 10546, Iowa State University, Department of Economics.
  3. Arthur, W Brian, 1993. "On Designing Economic Agents That Behave Like Human Agents," Journal of Evolutionary Economics, Springer, vol. 3(1), pages 1-22, February.
  4. Birchenhall, Chris, 1995. "Modular Technical Change and Genetic Algorithms," Computational Economics, Society for Computational Economics, vol. 8(3), pages 233-53, August.
  5. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-71, May.
  6. Lucas, Robert E, Jr, 1986. "Adaptive Behavior and Economic Theory," The Journal of Business, University of Chicago Press, vol. 59(4), pages S401-26, October.
  7. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  8. repec:fth:simfra:92-17 is not listed on IDEAS
  9. Jack Hirshleifer, 1977. "Economics from a Biological Viewpoint," UCLA Economics Working Papers 087, UCLA Department of Economics.
  10. Ramon Marimon & Ellen McGrattan, 1993. "On adaptive learning in strategic games," Economics Working Papers 24, Department of Economics and Business, Universitat Pompeu Fabra.
  11. Marks, R E, 1992. "Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists," Journal of Evolutionary Economics, Springer, vol. 2(1), pages 17-38, March.
  12. Eaton, C. & White, W.D., 1992. "Image Building," Discussion Papers dp92-17, Department of Economics, Simon Fraser University.
  13. Hirshleifer, Jack, 1977. "Economics from a Biological Viewpoint," Journal of Law and Economics, University of Chicago Press, vol. 20(1), pages 1-52, April.
  14. Beaumont, Paul M & Bradshaw, Patrick T, 1995. "A Distributed Parallel Genetic Algorithm for Solving Optimal Growth Models," Computational Economics, Society for Computational Economics, vol. 8(3), pages 159-79, August.
  15. Kiyotaki, Nobuhiro & Wright, Randall, 1989. "On Money as a Medium of Exchange," Journal of Political Economy, University of Chicago Press, vol. 97(4), pages 927-54, August.
  16. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
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Cited by:
  1. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.

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