Robust design in multivariate systems using genetic algorithms

Allende, Héctor; Bravo, Daniela; Canessa, Enrique

doi:10.1007/s11135-008-9201-z

Cited by 9 publications

(28 citation statements)

References 8 publications

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“…More details of the GA may be found in [7,8]. The GA developed in [8] represents the combinations of k control factors that may take s different levels (values) of a robust design experiment using an integer codification.…”

Section: Some Details Of the Genetic Algorithm Used In Robust Designmentioning

confidence: 99%

“…However, one can also change the value of other parameters of the GA that were kept constant in previous studies [7,8].…”

Section: Experimental Design For the Ga's Performance Analysismentioning

confidence: 99%

“…A previous analysis suggested that the performance of the GA may be influenced by the experimental design that the robust design study uses [7]. Specifically, the level of fractioning in such design will determine the number of data points that the GA will have for beginning the search process.…”

Section: Identification Of Experimental Factorsmentioning

confidence: 99%

“…In order to be able to use previous studies [7,8] as a base scenario, one of the levels of the identified factors will be the same values used in those research papers. Regarding the fractioning levels, in [7,8], the authors applied an inner OA L 64 (4 10 ) for the 10 control factors and an outer OA L 16 (4 5 ) for the 5 noise factors. Thus, the product array has 64 times 16, 1024 design points.…”

Section: Experimental Design and Levels Of Factorsmentioning

confidence: 99%

“…The application of PD has had a profound worldwide effect on enhancing the quality of products and services in many industries [1][2][3][4][5][6]. To facilitate parameter design studies, the work presented in [7,8] developed a tool based on Genetic Algorithms (GA), which automatically finds the levels of control factors that achieve mean adjustment and low variability in all responses. Given that other researchers have also explored the application of GA to robust design (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Performance of a Genetic Algorithm applied to robust design in multiobjective systems under different levels of fractioning

Canessa

Allende

2015

Rev. Fac. Ing. Antioquia

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This paper studies the performance of a Genetic Algorithm (GA) to find solutions to problems of robust design in multiobjective systems with many control and noise factors, representing the output vector in a single aggregation function. The results show that the GA is able to find solutions that achieve a good adjustment of the responses to their corresponding target values and with low variability, even with highly fractional experimental designs, which provide a limited number of data points to be fed to the GA. This conclusion is important for the practical application of the GA to robust design studies. Generally, such studies are carried out using scarce resources and dealing with other limitations, which force the engineer to use few experimental treatments and gather a limited amount of data. Thus, knowing that the GA performs well under such situation expands its applicability. ----------Keywords: Taguchi methods, parameter design, genetic algorithms, performance analysis ResumenEste trabajo estudia el rendimiento de un Algoritmo Genético (AG) para encontrar soluciones a problemas de diseño robusto en sistemas multiobjetivo, con muchos factores de control y ruido, representando el vector de salida * Corresponding author: Enrique Canessa Terrazas, e-mail: ecanessa@uai.cl DOI: 10.17533/udea.redin.n75a09 81Performance of a Genetic Algorithm applied to robust design in multiobjective systems... en una sola función de agregación. Los resultados muestran que el AG es capaz de encontrar soluciones que entregan un buen ajuste de la media de las respuestas a sus respectivos valores objetivo y con baja variabilidad, incluso con diseños experimentales altamente fraccionados, los cuales proveen de un número limitado de datos que se ingresan al AG. Esta conclusión es importante para la aplicación práctica del AG a estudios de diseño robusto. Generalmente, dichos estudios son llevados a cabo usando recursos escasos y lidiando con otras limitaciones, lo que obliga al ingeniero a usar pocos tratamientos y recoger una cantidad limitada de datos. Por eso, saber que el AG se comporta bien bajo esas situaciones, expande su aplicabilidad.----------Palabras clave: métodos de Taguchi, diseño de parámetros, algoritmos genéticos, análisis de rendimiento

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Section: Some Details Of the Genetic Algorithm Used In Robust Designmentioning

confidence: 99%

“…However, one can also change the value of other parameters of the GA that were kept constant in previous studies [7,8].…”

Section: Experimental Design For the Ga's Performance Analysismentioning

confidence: 99%

Section: Identification Of Experimental Factorsmentioning

confidence: 99%