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., Farasat, A. (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
mohammad reza mehrgan; Ali Reza 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, -.
HARVARD
mehrgan, M., Farasat, A. (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. -.
VANCOUVER
mehrgan, M., Farasat, A. 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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