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

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Title of host publicationIEEE Congress on Evolutionary Computation, Edinburgh
DatePublished - 1 Sep 2005
Pages928-934
Number of pages7
PublisherIEEE
Place of PublicationNEW YORK
Volume1
Original languageEnglish
ISBN (Print)0-7803-9363-5

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.

    Research areas

  • HYDRAULIC DATA, EQUATIONS, EVOLUTION

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