Parametric approximation of functions using genetic algorithms: an example with a logistic curve

  • Authors:

  • Fernando Torrecilla-Pinero;Jesús A. Torrecilla-Pinero;Juan A. Gómez-Pulido;Miguel A. Vega-Rodríguez;Juan M. Sánchez-Pérez

  • Affiliations:

  • Dep. of Technologies of Computers and Communications, University of Extremadura, Spain;Dep. of Building, University of Extremadura, Spain;Dep. of Technologies of Computers and Communications, University of Extremadura, Spain;Dep. of Technologies of Computers and Communications, University of Extremadura, Spain;Dep. of Technologies of Computers and Communications, University of Extremadura, Spain

  • Venue:
  • NMA'10 Proceedings of the 7th international conference on Numerical methods and applications
  • Year:
  • 2010

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Abstract

Whenever we have a set of discrete measures of a phenomenon and try to find an analytic functionwhichmodels such phenomenon, we are solving a problem about finding some parameters that minimizes a computable error function. In this way, parameter estimation may be studied as an optimization problem, in which the fitness function we are trying to minimize is the error one. This work try to do that using a genetic algorithm to obtain three parameters of a function. Particularly, we use data about one village population over time to see the goodness of our algorithm.