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Author 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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Author Canessa, E.; Chaigneau, S.
Title Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design Type
Year 2017 Publication Ingenieria E Investigacion Abbreviated Journal Ing. Invest.
Volume 37 Issue 2 Pages 89-98
Keywords Robust design; parameter design; pareto genetic algorithm; response surface methodology
Abstract We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multi-response systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the trade-off among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs' means and variances in highly non-linear systems, making the new PGA appropriate for such systems.
Address [Canessa, Enrique] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: ecanessa@uai.cl;
Corporate Author Thesis
Publisher Univ Nac Colombia, Fac Ingenieria Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0120-5609 ISBN Medium
Area Expedition Conference
Notes WOS:000408441100012 Approved
Call Number UAI @ eduardo.moreno @ Serial 760
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Author Canessa, E.; Droop, C.; Allende, H.
Title An improved genetic algorithm for robust design in multivariate systems Type
Year 2012 Publication Quality & Quantity Abbreviated Journal Qual. Quant.
Volume 46 Issue 2 Pages 665-678
Keywords Robust design; Taguchi methods; Genetic algorithms; Desirability functions; Research article
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.
Address [Canessa, Enrique; Droop, Christian; Allende, Hector] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Balmaceda 1620, Vina Del Mar, Chile, Email: ecanessa@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:000299134200017 Approved
Call Number UAI @ eduardo.moreno @ Serial 191
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Author Canessa, E.; Vera, S.; Allende, H.
Title A new method for estimating missing values for a genetic algorithm used in robust design Type
Year 2012 Publication Engineering Optimization Abbreviated Journal Eng. Optimiz.
Volume 44 Issue 7 Pages 787-800
Keywords robust design; Taguchi methods; genetic algorithms; response surface methodology
Abstract This article presents an improved genetic algorithm (GA), which finds solutions to problems of robust design in multivariate systems with many control and noise factors. Since some values of responses of the system might not have been obtained from the robust design experiment, but may be needed in the search process, the GA uses response surface methodology (RSM) to estimate those values. In all test cases, the GA delivered solutions that adequately adjusted the mean of the responses to their corresponding target values and with low variability. The GA found more solutions than the previous versions of the GA, which makes it easier to find a solution that may meet the trade-off among variance reduction, mean adjustment and economic considerations. Moreover, RSM is a good method for estimating the mean and variance of the outputs of highly non-linear systems, which makes the new GA appropriate for optimizing such systems.
Address [Canessa, E.; Vera, S.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Vina Del Mar, Recreo, Chile, Email: ecanessa@uai.cl
Corporate Author Thesis
Publisher Taylor & Francis Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0305-215x ISBN Medium
Area Expedition Conference
Notes WOS:000305212800002 Approved
Call Number UAI @ eduardo.moreno @ Serial 218
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