Please use this identifier to cite or link to this item:
|Title:||Some improvements on adaptive genetic algorithms for reliability-related applications|
|Keywords:||Adaptive genetic algorithm|
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 18, 2018
WEB OF SCIENCETM
checked on Oct 10, 2018
checked on Aug 17, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.