The use of a genetic algorithm to optimize the functional form of a multi-dimensional polynomial fit to experimental data

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Abstract

This paper begins with the optimisation of three test functions using a genetic algorithm and describes a statistical analysis on the effects of the choice of crossover technique, parent selection strategy and mutation. The paper then examines the use of a genetic algorithm to optimize the functional form of a polynomial fit to experimental data; the aim being to locate the global optimum of the data. Genetic programming has already been used to locate the functional form of a good fit to sets of data, but genetic programming is more complex than a genetic algorithm. This paper compares the genetic algorithm method with a particular genetic programming approach and shows that equally good results can be achieved using this simpler technique.

Original languageEnglish
Title of host publicationIEEE Congress on Evolutionary Computation, Edinburgh
Place of PublicationNEW YORK
PublisherIEEE
Pages928-934
Number of pages7
Volume1
ISBN (Print)0-7803-9363-5
DOIs
Publication statusPublished - 1 Sep 2005
EventIEEE Congress on Evolutionary Computation - Edinburgh
Duration: 2 Sep 20055 Sep 2005

Conference

ConferenceIEEE Congress on Evolutionary Computation
CityEdinburgh
Period2/09/055/09/05

Keywords

  • HYDRAULIC DATA
  • EQUATIONS
  • EVOLUTION

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