A genetic approach to econometric modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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",
  address =      "Barcelona, Spain",
  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
                 'genetic programming' paradigm genetically breeds
                 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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