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Author (up) Allende, H.; Bravo, D.; Canessa, E.
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: hallende@uai.cl
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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