Computational Economics

, Volume 8, Issue 3, pp 159–179 | Cite as

A distributed parallel genetic algorithm for solving optimal growth models

  • Paul M. Beaumont
  • Patrick T. Bradshaw


We use a distributed parallel genetic algorithm (DPGA) to fund numerical solutions to a single state deterministic optimal growth model for both the infinite and finite horizon cases. To evaluate the DPGA we consider a version of the Taylor-Uhlig problem for which the analytical solutions are known. The first-order conditions for the infinite horizon case lead to a nonlinear integral equation whose solution we approximate using a Chebyshev polynomial series expansion. The DPGA is used to search the parameter space for the optimal fit for this function. For the finite horizon case the DPGA searches the state space for a sequence of states which maximize the agent's discounted utility over the finite horizon. The DPGA runs on a cluster of up to fifty workstations linked via PVM. The topology of the function to be optimized is mapped onto each node on the cluster and the nodes essentially complete with one another for the optimal solution. We demonstrate that the DPGA has several useful features. For instance, the DPGA solves the exact Euler equation over the full range of the state variable and it does not require an accurate initial guess. The DPGA is easily generalized to multiple state problems.


Series Expansion Euler Equation Initial Guess Single State Multiple State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Paul M. Beaumont
    • 1
  • Patrick T. Bradshaw
    • 1
  1. 1.Supercomputer Computations Research Institute and Department of EconomicsFlorida State UniversityTallahasseeUSA

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