The Use of Genetic Algorithms in Response Surface Methodology

  1. Álvarez, M. J. 1
  2. Ilzarbe, L. 1
  3. Viles, E. 1
  4. Tanco, M. 1
  1. 1 Universidad de Navarra
    info

    Universidad de Navarra

    Pamplona, España

    ROR https://ror.org/02rxc7m23

Revista:
Quality Technology and Quantitative Management

ISSN: 1684-3703

Any de publicació: 2009

Volum: 6

Número: 3

Pàgines: 295-307

Tipus: Article

DOI: 10.1080/16843703.2009.11673201 GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: Quality Technology and Quantitative Management

Resum

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.

Referències bibliogràfiques

  • 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.
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  • 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.