Robust design in multivariate systems using genetic algorithms

Hector Allende, Daniela Bravo, Enrique Canessa

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This paper presents a methodology based on genetic algorithms, which finds feasible and reasonably adequate solutions to problems of robust design in multivariate systems. We use a genetic algorithm to determine the appropriate control factor levels for simultaneously optimizing all of the responses of the system, considering the noise factors which affect it. The algorithm is guided by a desirability function which works with only one fitness function although the system may have many responses. We validated the methodology using data obtained from a real system and also from a process simulator, considering univariate and multivariate systems. In all cases, the methodology delivered feasible solutions, which accomplished the goals of robust design: obtain responses very close to the target values of each of them, and with minimum variability. Regarding the adjustment of the mean of each response to the target value, the algorithm performed very well. However, only in some of the multivariate cases, the algorithm was able to significantly reduce the variability of the responses.

Original languageEnglish
Pages (from-to)315-332
Number of pages18
JournalQuality and Quantity
Issue number2
StatePublished - Feb 2010


  • Desirability functions
  • Genetic algorithms
  • Robust design
  • Taguchi methods


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