DocumentCode :
2727778
Title :
The use of a genetic algorithm to optimize the functional form of a multi-dimensional polynomial fit to experimental data
Author :
Clegg, Janet ; Dawson, John F. ; Porter, Stuart J. ; Barley, Mark H.
Author_Institution :
Dept. of Electron., Univ. of York
Volume :
1
fYear :
2005
fDate :
5-5 Sept. 2005
Firstpage :
928
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 :
genetic algorithms; polynomials; statistical analysis; crossover technique; genetic algorithm; genetic programming; multidimensional polynomial fit; mutation; optimisation; parent selection strategy; statistical analysis; Genetic algorithms; Genetic mutations; Genetic programming; Least squares methods; Optimization methods; Polynomials; Sampling methods; Statistical analysis; Surface fitting; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Conference_Location :
Edinburgh, Scotland
Print_ISBN :
0-7803-9363-5
Type :
conf
DOI :
10.1109/CEC.2005.1554782
Filename :
1554782
Link To Document :
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