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Functional Search in Economics Using Genetic Programming

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Author Info

  • Schmertmann, Carl P

Abstract

This paper discusses economic applications of a recently developed artificial intelligence technique-Koza's genetic programming (GP). GP is an evolutionary search method related to genetic algorithms. In GP, populations of potential solutions consist of executable computer algorithms, rather than coded strings. The paper provides an overview of how GP works, and illustrates with two applications: solving for the policy function in a simple optimal growth model, and estimating an unusual regression function. Results suggest that the GP search method can be an interesting and effective tool for economists. Citation Copyright 1996 by Kluwer Academic Publishers.

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 9 (1996)
Issue (Month): 4 (November)
Pages: 275-98

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Handle: RePEc:kap:compec:v:9:y:1996:i:4:p:275-98

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Cited by:
  1. Paul McNelis & John Duffy, 1998. "Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm," GE, Growth, Math methods 9804004, EconWPA, revised 04 May 1998.
  2. Peter Woehrmann & Willi Semmler & Martin Lettau, . "Nonparametric Estimation of the Time-varying Sharpe Ratio in Dynamic Asset Pricing Models," IEW - Working Papers 225, Institute for Empirical Research in Economics - University of Zurich.
  3. Bernd Ebersberger & Uwe Cantner & Horst Hanusch, 2000. "Analyzing Inefficiency Using a Frontier Search Approach," Discussion Paper Series 199, Universitaet Augsburg, Institute for Economics.
  4. Uwe Cantner & Bernd Ebersberger & Horst Hanusch & Jens J. Krüger & Andreas Pyka, 2004. "The Twin Peaks in National Income. Parametric and Nonparametric Estimates," Revue économique, Presses de Sciences-Po, vol. 55(6), pages 1127-1144.
  5. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.

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