# A genetic approach to econometric modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.3989

```@InProceedings{Koza:1990:gem,
author =       "John R. Koza",
title =        "A genetic approach to econometric modeling",
booktitle =    "Sixth World Congress of the Econometric Society",
year =         "1990",
keywords =     "genetic algorithms, genetic programming",
URL =          "http://www.genetic-programming.com/jkpdf/wces1990.pdf",
size =         "33 pages",
abstract =     "An important problem in economics and other areas of
science is finding the mathematical relationship
between the empirically observed variables measuring a
system. In many conventional modelling techniques, one
necessarily begins by selecting the size and shape of
the mathematical model. Because the vast majority of
available mathematical tools only handle linear models,
this choice is often simply a linear model. After
making this choice, one usually then tries to find the
values of certain coefficients and constants required
by the particular model so as to achieve the best fit
between the observed data and the model. But, in many
cases, the most important issue is the size and shape
of the mathematical model itself. That is, one really
wants first to find the functional form of the model
that best fits observed empirical data, and, only then,
go on to find any constants and coefficients that
happen to be needed. Some techniques exist for doing
this. We suggest that finding the functional form of
the model can productively be viewed as being
equivalent to searching a space of possible computer
programs for the particular individual computer program
which produces the desired output for given input. That
is, one is searching for the computer program whose
behaviour best fits the observed data. Computer
programs offer great flexibility in the ways that they
compute their output from the given inputs. The most
fit individual computer program can be found via a new
'genetic programming' paradigm originally developed for
solving artificial intelligence problems. This new
populations of computer programs in a Darwinian
competition using genetic operations. The Darwinian
competition is based on the principle of survival and
reproduction of the fittest. The genetic crossover
(sexual recombination) operator is designed for
genetically mating computer programs so as to create
potentially more fit new offspring programs. The best
single individual computer program produced by this
process after many generations can be viewed as the
solution to the problem. In this paper, we illustrate
the process of formulating and solving problems of
modeling (i.e. symbolic regression, symbolic function
identification) with this new 'genetic programming'
paradigm using hierarchical genetic algorithms. In
particular, the 'genetic programming' paradigm is
illustrated by rediscovering the well-known
multiplicative (non-linear) 'exchange equation' M=PQ/V
relating the money supply, price level, gross national
product, and velocity of money in an economy.",
notes =        "27 August",
}

```

Genetic Programming entries for John Koza

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