A genetic algorithm calibration method based on convergence due to genetic drift

  • Authors:

  • Matthew S. Gibbs;Graeme C. Dandy;Holger R. Maier

  • Affiliations:

  • School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide SA 5005, Australia;School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide SA 5005, Australia;School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide SA 5005, Australia

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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Abstract

The selection of Genetic Algorithm (GA) parameters is a difficult problem, and if not addressed adequately, solutions of good quality are unlikely to be found. A number of approaches have been developed to assist in the calibration of GAs, however there does not exist an accepted method to determine the parameter values. In this paper, a GA calibration methodology is proposed based on the convergence of the population due to genetic drift, to allow suitable GA parameter values to be determined without requiring a trial-and-error approach. The proposed GA calibration method is compared to another GA calibration method, as well as typical parameter values, and is found to regularly lead the GA to better solutions, on a wide range of test functions. The simplicity and general applicability of the proposed approach allows suitable GA parameter values to be estimated for a wide range of situations.