Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19979
Title: A fuzzy genetic algorithm for the discovery of process parameter settings using knowledge representation
Authors: Lau, HCW
Tang, CXH
Ho, GTS
Chan, TM
Keywords: Evolutionary computing
Fuzzy set
Genetic algorithms
Inverted beta loss function
Knowledge representation
Reactive ion etching
Issue Date: 2009
Publisher: Pergamon Press
Source: Expert systems with applications, 2009, v. 36, no. 4, p. 7964-7974 How to cite?
Journal: Expert systems with applications 
Abstract: In this paper, we propose a fuzzy genetic algorithm (Fuzzy-GA) approach integrating fuzzy rule sets and their membership function sets, in a chromosome. The proposed approach consists of two processes: knowledge representation and knowledge assimilation. The knowledge of process parameter setting is encoded as a string with a fuzzy rule set and the associated membership functions. The historical process data forming a combined string is used as the initial knowledge population, which is then ready for knowledge assimilation. A genetic algorithm is used to generate an optimal or nearly optimal fuzzy set and membership functions for the process parameters. The originality of this research is that the proposed system is equipped with the ability to take advantage of assessing the loss which is caused by discrepancy with a process target, thereby enabling the identification of the best set of process parameters. The approach is demonstrated by the use of an experimental example drawn from a semiconductor manufacturer and the results show us that the suggested approach is able to achieve an optimal solution for a process parameter setting problem.
URI: http://hdl.handle.net/10397/19979
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2008.10.088
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

7
Last Week
0
Last month
1
Citations as of Aug 18, 2017

WEB OF SCIENCETM
Citations

7
Last Week
0
Last month
1
Citations as of Aug 20, 2017

Page view(s)

27
Last Week
0
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.