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Yong Zhang, Xi-Xiang Chen, Guan-Jun Liu, Jing Qiu, Shu-Ming Yang. Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm[J]. Journal of Electronic Science and Technology, 2009, 7(4): 317-321.
Citation: Yong Zhang, Xi-Xiang Chen, Guan-Jun Liu, Jing Qiu, Shu-Ming Yang. Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm[J]. Journal of Electronic Science and Technology, 2009, 7(4): 317-321.
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Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm

  • College of Mechatronical Engineering and Automation,National University of Defense Technology,Changsha,410073,China

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This work was supported by the Advanced Research Project of a National Department of China under Grant No. 51317040102.

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  • Author Bio:

    Yong Zhang research interests include DFT and fault diagnosis, zhangy21cn@126.com;
    Xi-Xiang Chen, chen_xixiang@126.com;
    Guan-Jun Liu, gjliu342@sina.com;
    Jing Qiu, qiujing@nudt.edu.cn;
    Shu-Ming Yang, ysmcsu@163.com

    Yong Zhang research interests include DFT and fault diagnosis, zhangy21cn@126.com;
    Xi-Xiang Chen, chen_xixiang@126.com;
    Guan-Jun Liu, gjliu342@sina.com;
    Jing Qiu, qiujing@nudt.edu.cn;
    Shu-Ming Yang, ysmcsu@163.com

    Yong Zhang research interests include DFT and fault diagnosis, zhangy21cn@126.com;
    Xi-Xiang Chen, chen_xixiang@126.com;
    Guan-Jun Liu, gjliu342@sina.com;
    Jing Qiu, qiujing@nudt.edu.cn;
    Shu-Ming Yang, ysmcsu@163.com

    Yong Zhang research interests include DFT and fault diagnosis, zhangy21cn@126.com;
    Xi-Xiang Chen, chen_xixiang@126.com;
    Guan-Jun Liu, gjliu342@sina.com;
    Jing Qiu, qiujing@nudt.edu.cn;
    Shu-Ming Yang, ysmcsu@163.com

    Yong Zhang research interests include DFT and fault diagnosis, zhangy21cn@126.com;
    Xi-Xiang Chen, chen_xixiang@126.com;
    Guan-Jun Liu, gjliu342@sina.com;
    Jing Qiu, qiujing@nudt.edu.cn;
    Shu-Ming Yang, ysmcsu@163.com

  • Rev Recd Date: 2009-07-23
  • Publish Date: 2009-12-24
    Abstract
  • A new approach to select an optimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems.
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