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Functional Search in Economics Using Genetic Programming

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  • 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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  • Schmertmann, Carl P, 1996. "Functional Search in Economics Using Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 9(4), pages 275-298, November.
    • Handle: RePEc:kap:compec:v:9:y:1996:i:4:p:275-98
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    Cited by:

    1. Mariano Matilla-Garcia, 2006. "Are trading rules based on genetic algorithms profitable?," Applied Economics Letters, Taylor & Francis Journals, vol. 13(2), pages 123-126.
    2. 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.
    3. Duffy, John & McNelis, Paul D., 2001. "Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 25(9), pages 1273-1303, September.
    4. Vinícius Ferraz & Thomas Pitz, 2024. "Analyzing the Impact of Strategic Behavior in an Evolutionary Learning Model Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 437-475, February.
    5. 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.
    6. Peter Woehrmann & Willi Semmler & Martin Lettau, "undated". "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.
    7. Mariano Matilla-Garcia, 2005. "A note on cointegrated relationships estimated with genetic algorithms," Applied Economics Letters, Taylor & Francis Journals, vol. 12(4), pages 235-238.
    8. Bernd Ebersberger & Uwe Cantner & Horst Hanusch, 2000. "Analyzing Inefficiency Using a Frontier Search Approach," Discussion Paper Series 199, Universitaet Augsburg, Institute for Economics.

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