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. Journal of Information Technology Management, 1(1), -.
MLA
mehrgan, M. R. , and Farasat, A. R. . "A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering", Journal of Information Technology Management, 1, 1, 2009, -.
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), pp. -.
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): -,
VANCOUVER
mehrgan, M. R., Farasat, A. R. A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering. Journal of Information Technology Management, 2009; 1(1): -.
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