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Functional Search in Economics Using Genetic Programming

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Author Info
Schmertmann, Carl P

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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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Publisher Info
Article provided by Springer 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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Web page: http://www.springerlink.com/link.asp?id=100248

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  1. Peter Woehrmann & Willi Semmler & Martin Lettau, . "Nonparametric Estimation of the Time-varying Sharpe Ratio in Dynamic Asset Pricing Models," IEW - Working Papers iewwp225, Institute for Empirical Research in Economics - IEW. [Downloadable!]
  2. Bernd Ebersberger & Uwe Cantner & Horst Hanusch, 2000. "Analyzing Inefficiency Using a Frontier Search Approach," Discussion Paper Series 199, Universitaet Augsburg, Institute for Economics. [Downloadable!]
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This page was last updated on 2009-12-10.


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