An improved genetic algorithm for robust design in multivariate systems

Enrique Canessa, Christian Droop, Hector Allende

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)665-678
Number of pages14
JournalQuality and Quantity
Issue number2
StatePublished - Feb 2012
Externally publishedYes


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


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