• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Related Links
    • FAQ
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter
Journal of Information Technology Management
Articles in Press
Current Issue
Journal Archive
Volume Volume 11 (2019)
Volume Volume 10 (2018)
Volume Volume 9 (2017)
Volume Volume 8 (2016)
Volume Volume 7 (2015)
Volume Volume 6 (2014)
Volume Volume 5 (2013)
Volume Volume 4 (2012)
Volume Volume 3 (2011)
Volume Volume 2 (2010)
Volume Volume 1 (2009)
Issue Issue 3
Summer 2009
Issue Issue 2
Spring 2009
Issue Issue 1
Winter 2009
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), -.
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, -.
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. -.
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): -.

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

Article 8, Volume 1, Issue 1, Winter 2009  XML PDF (219.49 K)
Authors
mohammad reza mehrgan; Ali Reza Farasat*
Abstract
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.
Keywords
Co evolution Genetic Algorithm; Genetic algorithm; neural networks; Quality Engineering; Robust optimization
Statistics
Article View: 3,222
PDF Download: 2,422
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

hit
counter
Journal Management System. Designed by sinaweb.