Clegg, Janet, Dawson, John orcid.org/0000-0003-4537-9977, Porter, Stuart et al. (1 more author) (2005) The use of a genetic algorithm to optimize the functional form of a multi-dimensional polynomial fit to experimental data. In: IEEE Congress on Evolutionary Computation, Edinburgh. IEEE Congress on Evolutionary Computation, 02-05 Sep 2005 IEEE , Edinburgh , pp. 928-934.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | HYDRAULIC DATA,EQUATIONS,EVOLUTION |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number ICI PLC UNSPECIFIED ICI PLC UNSPECIFIED |
Depositing User: | Pure (York) |
Date Deposited: | 12 Jul 2018 08:50 |
Last Modified: | 23 Jan 2025 00:38 |
Published Version: | https://doi.org/10.1109/CEC.2005.1554782 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CEC.2005.1554782 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132802 |