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A Distributed Parallel Genetic Algorithm: An Application from Economic Dynamics

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Computational Economic Systems

Part of the book series: Advances in Computational Economics ((AICE,volume 5))

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Abstract

We provide a brief overview of genetic algorithms and describe our distributed parallel genetic algorithm (DPGA) which substantially overcomes the problem of premature convergence often encountered in serial genetic algorithms. The DPGA is used to solve an infinite horizon optimal growth model which has become a standard test case for algorithms in the economic dynamics literature. The DPGA is shown to be easy to use and to produce good solutions. The flexibility of the DPGA is demonstrated by solving the model using several different sets of basis functions and evaluating the quality of the solutions. We find that the choice of bases is quite important and that numerical analysis issues provide the critical factors in this choice.

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Authors
  1. Paul M. Beaumont
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  2. Patrick T. Bradshaw
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Editor information

Editors and Affiliations

  1. Department of Econometrics, University of Geneva, Geneva, Switzerland

    Manfred Gilli

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© 1996 Springer Science+Business Media Dordrecht

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Beaumont, P.M., Bradshaw, P.T. (1996). A Distributed Parallel Genetic Algorithm: An Application from Economic Dynamics. In: Gilli, M. (eds) Computational Economic Systems. Advances in Computational Economics, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8743-3_4

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  • DOI: https://doi.org/10.1007/978-94-015-8743-3_4

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-4655-0

  • Online ISBN: 978-94-015-8743-3

  • eBook Packages: Springer Book Archive

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