publication . Article . 2009

A Hybrid Neural Networks-Coevolution Genetic Algorithm for Multi Variables Robust Design Problem in Quality Engineering

Open Access Persian
  • Published: 01 Feb 2009 Journal: Journal of Information Technology Management, volume 1, issue 1 (issn: 2008-5893, eissn: 2423-5059, Copyright policy)
  • Publisher: University of Tehran
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.
arXiv: Computer Science::Neural and Evolutionary Computation
free text keywords: Co evolution Genetic Algorithm, Genetic algorithm, neural networks, Quality Engineering, Robust optimization, lcsh:Information resources (General), lcsh:ZA3040-5185
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