In this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. The proposed algorithm comprises neural networks (NNs) and co-evolution genetic algorithm (CGA) in which neural networks are as a function approximation tool used to estimate a map between process variables. Furthermore, in order to make a robust optimization of response variables, co-evolution algorithm is applied to solve constructed model of process. Results of CGA are compared with genetic algorithm (GA). This algorithm is tested in a case study of open-end spinning process.
mehrgan,M R and Farasat,A R . (2009). A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering. (e27732). Journal of Information Technology Management, 1(1), e27732
MLA
mehrgan,M R , and Farasat,A R . "A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering" .e27732 , Journal of Information Technology Management, 1, 1, 2009, e27732.
HARVARD
mehrgan M R, Farasat A R. (2009). 'A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering', Journal of Information Technology Management, 1(1), e27732.
CHICAGO
M R mehrgan and A R Farasat, "A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering," Journal of Information Technology Management, 1 1 (2009): e27732,
VANCOUVER
mehrgan M R, Farasat A R. A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering. JITM. 2009;1(1):e27732.
