Some improvements on adaptive genetic algorithms for reliability-related applications
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 120-126 |
Journal / Publication | Reliability Engineering and System Safety |
Volume | 95 |
Issue number | 2 |
Publication status | Published - Feb 2010 |
Externally published | Yes |
Link(s)
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.
Research Area(s)
- Adaptive genetic algorithm, Population disturbance, Preventive maintenance
Citation Format(s)
Some improvements on adaptive genetic algorithms for reliability-related applications. / Ye, Zhisheng; Li, Zhizhong; Xie, Min.
In: Reliability Engineering and System Safety, Vol. 95, No. 2, 02.2010, p. 120-126.
In: Reliability Engineering and System Safety, Vol. 95, No. 2, 02.2010, p. 120-126.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review