Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ress.2009.09.001
Title: Some improvements on adaptive genetic algorithms for reliability-related applications
Authors: Ye, Z.
Li, Z.
Xie, M. 
Keywords: Adaptive genetic algorithm
Population disturbance
Preventive maintenance
Issue Date: Feb-2010
Source: Ye, Z., Li, Z., Xie, M. (2010-02). Some improvements on adaptive genetic algorithms for reliability-related applications. Reliability Engineering and System Safety 95 (2) : 120-126. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ress.2009.09.001
Abstract: Adaptive genetic algorithms (GAs) have been shown to be able to improve GA performance in reliability-related optimization studies. However, there are different ways to implement adaptive GAs, some of which are even in conflict with each other. In this study, a simple parameter-adjusting method using mean and variance of each generation is introduced. This method is used to compare two of such conflicting adaptive GA methods: GAs with increasing mutation rate and decreasing crossover rate and GAs with decreasing mutation rate and increasing crossover rate. The illustrative examples indicate that adaptive GAs with decreasing mutation rate and increasing crossover rate finally yield better results. Furthermore, a population disturbance method is proposed to avoid local optimum solutions. This idea is similar to exotic migration to a tribal society. To solve the problem of large solution space, a variable roughening method is also embedded into GA. Two case studies are presented to demonstrate the effectiveness of the proposed method. © 2009 Elsevier Ltd. All rights reserved.
Source Title: Reliability Engineering and System Safety
URI: http://scholarbank.nus.edu.sg/handle/10635/63330
ISSN: 09518320
DOI: 10.1016/j.ress.2009.09.001
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

48
checked on Dec 14, 2017

WEB OF SCIENCETM
Citations

36
checked on Nov 18, 2017

Page view(s)

19
checked on Dec 17, 2017

Google ScholarTM

Check

Altmetric


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