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Some improvements on adaptive genetic algorithms for reliability-related applications

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  • Ye, Zhisheng
  • Li, Zhizhong
  • Xie, Min

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.

Suggested Citation

  • Ye, Zhisheng & Li, Zhizhong & Xie, Min, 2010. "Some improvements on adaptive genetic algorithms for reliability-related applications," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 120-126.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:2:p:120-126
    DOI: 10.1016/j.ress.2009.09.001
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    Cited by:

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    2. Wu, Xin-yang & Wu, Xiao-yue & Balakrishnan, Narayanaswamy, 2018. "Reliability allocation model and algorithm for phased mission systems with uncertain component parameters based on importance measure," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 266-276.
    3. Peng, Rui & Mo, Huadong & Xie, Min & Levitin, Gregory, 2013. "Optimal structure of multi-state systems with multi-fault coverage," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 18-25.
    4. Yang, Bo & Li, Xiang & Xie, Min & Tan, Feng, 2010. "A generic data-driven software reliability model with model mining technique," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 671-678.
    5. Zhai, Qingqing & Yang, Jun & Zhao, Yu, 2014. "Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 66-82.
    6. Ye, Zhi-Sheng & Peng, Rui & Wang, Wenbin, 2017. "Defense and attack of performance-sharing common bus systemsAuthor-Name: Zhai, Qingqing," European Journal of Operational Research, Elsevier, vol. 256(3), pages 962-975.
    7. Faghih-Roohi, Shahrzad & Xie, Min & Ng, Kien Ming & Yam, Richard C.M., 2014. "Dynamic availability assessment and optimal component design of multi-state weighted k-out-of-n systems," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 57-62.
    8. Hui Xiao & Rui Peng & Wenbin Wang & Fei Zhao, 2016. "Optimal element loading for linear sliding window systems," Journal of Risk and Reliability, , vol. 230(1), pages 75-84, February.

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