a hybrid neural networks-coevolution genetic algorithm for multi variables robust design problem in quality engineering

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.