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Author (up) Allende, H.; Bravo, D.; Canessa, E. pdf  doi
  Title Robust design in multivariate systems using genetic algorithms Type
  Year 2010 Publication Quality & Quantity Abbreviated Journal Qual. Quant.  
  Volume 44 Issue 2 Pages 315-332  
  Keywords Robust design; Taguchi methods; Genetic algorithms; Desirability functions  
  Abstract This paper presents a methodology based oil 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.  
  Address [Allende, Hector; Bravo, Daniela; Canessa, Enrique] Univ Adolfo Ibanez, Fac Ciencia & Tecnol, Balmaceda 1620, Vina Del Mar, Chile, Email:  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0033-5177 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000275327300008 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 82  
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