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 variab...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Tehran
2009-02-01
|
| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_27732_03d44ea0b0faab46550b544e73875b7e.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2008-5893 2423-5059 |