The Use of Genetic Algorithms in Response Surface Methodology
- Álvarez, M. J. 1
- Ilzarbe, L. 1
- Viles, E. 1
- Tanco, M. 1
-
1
Universidad de Navarra
info
ISSN: 1684-3703
Année de publication: 2009
Volumen: 6
Número: 3
Pages: 295-307
Type: Article
D'autres publications dans: Quality Technology and Quantitative Management
Résumé
Response Surface Methodology is a combination of experimental designs and statistical techniques for empirical model building and optimisation which has been applied to a wide range of fields. Recently, genetic algorithms have been developed to solve optimisation in stages where response surface models require maximization or minimization. GA's are evolutionary algorithms which have been found to be very useful at interfacing with response surfaces, as recent results in scientific literature testify. This paper shows how Genetic Algorithms can be used when RSM is applied and an optimisation process is required. A review of the recent scientific literature has been carried out. Some of these references are presented with the purpose of showing the use of Genetic Algorithms in practical applications of RSM.
Références bibliographiques
- Box, G. E. P. and Draper, N. R. (1987).Empirical Model Building and Response Surfaces.John Wiley and Sons, Inc.; New York, USA.
- Goldberg, D. E. (1989).Genetic Algorithm in Search, Optimization and Machine Learning.Addison-Wesley, Reading. MA.
- Holland, J. (1975).Adaptation in Natural and Artificial Systems.The University of Michigan Press, Ann Arbor. MI.
- Holland, J. (1992). Genetic algorithms.Scientific American, July, 44–55.
- Montgomery, D. C. (2005).Design and Analysis of Experiments. 6thed. John Wiley and Sons, Inc.; New York, USA.
- Myers, R. H. and Montgomery, D. C. (2002).Response Surface Methodology. 2nded.John Wiley and Sons, Inc.; New York, USA.