TY - JOUR
T1 - A new method for estimating missing values for a genetic algorithm used in robust design
AU - Canessa, E.
AU - Vera, S.
AU - Allende, H.
N1 - Funding Information:
The authors would like to thank Rick L. Riolo, Center for the Study of Complex Systems, The University of Michigan, and Claudio Moraga, European Centre for Soft Computing and FB Informatik Universität, Dortmund, for their valuable comments regarding the original version of this article. This work was supported in part by Research Grants 1110854 Fondecyt and by FB081 Centro Tecnológico de Valparaíso.
PY - 2012/7/1
Y1 - 2012/7/1
N2 - 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.
AB - 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.
KW - Taguchi methods
KW - genetic algorithms
KW - response surface methodology
KW - robust design
UR - http://www.scopus.com/inward/record.url?scp=84862586994&partnerID=8YFLogxK
U2 - 10.1080/0305215X.2011.613464
DO - 10.1080/0305215X.2011.613464
M3 - Article
AN - SCOPUS:84862586994
SN - 0305-215X
VL - 44
SP - 787
EP - 800
JO - Engineering Optimization
JF - Engineering Optimization
IS - 7
ER -