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

Janet Clegg; John Dawson; Stuart Porter; Mark Barley

doi:10.1109/CEC.2005.1554782

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

Janet Clegg, John Dawson, Stuart Porter, Mark Barley

Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English
Title of host publication	IEEE Congress on Evolutionary Computation, Edinburgh
Place of Publication	NEW YORK
Publisher	IEEE
Pages	928-934
Number of pages	7
Volume	1
ISBN (Print)	0-7803-9363-5
DOIs	https://doi.org/10.1109/CEC.2005.1554782
Publication status	Published - 1 Sept 2005
Event	IEEE Congress on Evolutionary Computation - Edinburgh Duration: 2 Sept 2005 → 5 Sept 2005

Conference

Conference	IEEE Congress on Evolutionary Computation
City	Edinburgh
Period	2/09/05 → 5/09/05

Keywords

HYDRAULIC DATA
EQUATIONS
EVOLUTION

Access to Document

10.1109/CEC.2005.1554782Licence: Unspecified

Clegg2005a_postprint
© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 2.23 MBLicence: Other

1 Finished

Optimisationof Chemical Formulation
Porter, S. J., Dawson, J. F. & Clegg, J.
ICI PLC
1/10/03 → 30/09/06
Project: Research project (funded) › Research

Cite this

@inproceedings{e44b99647cab4a8ab63d6050a121c168,

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

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.",

keywords = "HYDRAULIC DATA, EQUATIONS, EVOLUTION",

author = "Janet Clegg and John Dawson and Stuart Porter and Mark Barley",

year = "2005",

month = sep,

day = "1",

doi = "10.1109/CEC.2005.1554782",

language = "English",

isbn = "0-7803-9363-5",

volume = "1",

pages = "928--934",

booktitle = "IEEE Congress on Evolutionary Computation, Edinburgh",

publisher = "IEEE",

address = "United States",

note = "IEEE Congress on Evolutionary Computation ; Conference date: 02-09-2005 Through 05-09-2005",

}

TY - GEN

T1 - The use of a genetic algorithm to optimize the functional form of a multi-dimensional polynomial fit to experimental data

AU - Clegg, Janet

AU - Dawson, John

AU - Porter, Stuart

AU - Barley, Mark

PY - 2005/9/1

Y1 - 2005/9/1

N2 - 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.

AB - 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.

KW - HYDRAULIC DATA

KW - EQUATIONS

KW - EVOLUTION

U2 - 10.1109/CEC.2005.1554782

DO - 10.1109/CEC.2005.1554782

M3 - Conference contribution

SN - 0-7803-9363-5

VL - 1

SP - 928

EP - 934

BT - IEEE Congress on Evolutionary Computation, Edinburgh

PB - IEEE

CY - NEW YORK

T2 - IEEE Congress on Evolutionary Computation

Y2 - 2 September 2005 through 5 September 2005

ER -

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

Abstract

Conference

Keywords

Access to Document

Projects

Optimisationof Chemical Formulation

Cite this