Skip to main content

Advertisement

Log in

An improved genetic algorithm for robust design in multivariate systems

  • Research Note
  • Published:
Quality & Quantity Aims and scope Submit manuscript

Abstract

In a previous article, we presented a genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems. Based on that GA, we developed a new GA that uses a new desirability function, based on the aggregation of the observed variance of the responses and the squared deviation between the mean of each response and its corresponding target value. Additionally, we also changed the crossover operator from a one-point to a uniform one. We used three different case studies to evaluate the performance of the new GA and also to compare it with the original one. The first case study involved using data from a univariate real system, and the other two employed data obtained from multivariate process simulators. In each of the case studies, the new GA delivered good solutions, which simultaneously adjusted the mean of each response to its corresponding target value. This performance was similar to the one of the original GA. Regarding variability reduction, the new GA worked much better than the original one. In all the case studies, the new GA delivered solutions that simultaneously decreased the standard deviation of each response to almost the minimum possible value. Thus, we conclude that the new GA performs better than the original one, especially regarding variance reduction, which was the main problem exhibited by the original GA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Allende, H., Canessa, E., Galbiati, J.: Diseño de Experimentos Industriales. Edit. Universidad Técnica Federico Santa María, Valparaíso (2005)

  • Allende, H., Bravo, D., Canessa, E.: Robust design in multivariate systems using genetic algorithms. Qual. Quant. J. (2008). doi:10.1007/s11135-008-9201-z

  • Bravo, D.: Desarrollo de una Herramienta basada en Algoritmos Genéticos para resolver problemas de Diseño Robusto en Sistemas Multivariados. Master thesis, Universidad Adolfo Ibáñez (2005)

  • De Mast J.: A methodological comparison of three strategies for quality improvement. Int. J. Qual. Reliab. Manag. 21(2), 198–213 (2004)

    Article  Google Scholar 

  • Del Castillo E., Montgomery D.C., McCarville D.R.: Modified desirability functions for multiple response optimization. J. Qual. Technol. 28, 337–345 (1996)

    Google Scholar 

  • Drickhamer, D.: BASF breaks through with statistics. Ind. Week, June (2002)

  • Droop, C.: Diseño Robusto en Sistemas Multivariados utilizando Algoritmos Genéticos guiados por la media y varianza de las respuestas del sistema. Master thesis, Universidad Adolfo Ibáñez (2008)

  • Foneseca, G., Flemming, P.J.: Genetic algorithms for multi-objective optimization: formulation, discussion, and generalization. In: Proc. 5th Intern. Conf. on Genetic Algorithms, pp. 416–423. Morgan Kaufmann, San Francisco (1993)

  • Gunter, B.: A perspective on the Taguchi methods. Qual. Prog. 44–52, June (1987)

  • Holland J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1974)

    Google Scholar 

  • Kackar R.N., Shoemaker A.C.: Robust design: a cost effective method for improving manufacturing processes. AT&T Tech. J. 65(2), 39–50 (1986)

    Google Scholar 

  • Leon R., Schoemaker A.C., Kackar R.N.: Performance measures independent of adjustment: an explanation and extension of Taguchi’s signal to noise ratios (with discussions). Technometrics 29(3), 253–285 (1987)

    Article  Google Scholar 

  • Lorenzen T., Anderson V.L.: Design of Experiments, a No-Name Approach. Marcel Dekker Inc., New York (1993)

    Google Scholar 

  • Maghsoodloo S., Chang C.: Quadratic loss functions and SNR for a bivariate response. J. Manuf. Syst. 20(1), 1–12 (2001)

    Article  Google Scholar 

  • Ortiz F., Simpson J., Pigniatello J. Jr, Heredia-Langner A.: A genetic algorithm approach to multiple-response optimization. J. Qual. Technol. 36(4), 432–449 (2004)

    Google Scholar 

  • Pignatiello J.: An overview of the strategy and tactics of Taguchi. IIE Trans. 20(3), 247–254 (1988)

    Article  Google Scholar 

  • Roy R.K.: Design of Experiments Using the Taguchi Approach. Wiley, New York (2001)

    Google Scholar 

  • Schaffer, J.D. Some experiments in machine learning using vector evaluated genetic algorithm. PhD Dissertation. Vanderbilt University, Nashville (1984)

  • Syswerda G.: Uniform crossover in genetic algorithms. In: J.D., Schaffer (eds) Proc. of the 3rd Intern. Conf. on Genetic Algorithms, Van Nostrand Reinhold, New York (1989)

    Google Scholar 

  • Taguchi G.: Systems of experimental design. American Supplier Institute, Dearborn (1991)

    Google Scholar 

  • Vandenbrande, W.: Make love, not war: combining DOE and Taguchi. In: ASQ’s 54th Annu. Qual. Congr. Proc., pp. 450–456. (2000)

  • Yuan Y.: Multiple Imputation for Missing Data: Concepts and New Development. SAS Institute Inc., Rockville (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Enrique Canessa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Canessa, E., Droop, C. & Allende, H. An improved genetic algorithm for robust design in multivariate systems. Qual Quant 46, 665–678 (2012). https://doi.org/10.1007/s11135-010-9420-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11135-010-9420-y

Keywords

Navigation