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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/27356
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/27356

    Title: Genetic algorithms for MD-optimal follow-up designs
    Authors: 陳春龍;Rong-Ho Lin;Jianping Zhang
    Chen, Chun-Lung
    Keywords: Fractional factorial designs;Follow-up designs;Genetic algorithms
    Date: 2003-02
    Issue Date: 2009-01-17 16:34:50 (UTC+8)
    Abstract: The 2k−p fractional factorial design is the most widely used technique for industrial experimentation. This is because it can significantly reduce the number of experimental runs so that the application of experimental design to problems with a large number of factors becomes possible. However, the application of this technique usually causes the loss of important information. That is, some effects of the experiment may confound with each other and cannot be clearly identified. The follow-up design is a tool used to untangle the confounded effects produced in the initial experiment. In this research, a heuristic based on an effective evolutionary algorithm, Genetic Algorithms, has been developed to generate the optimal follow-up design. The heuristic has been applied in two common test examples. The result showed that the heuristic could simply find optimal follow-up designs, and dominate the existing algorithm. Genetic algorithms (GA) are probabilistic search techniques for optimization problems. In the past decade, more than a thousand technical papers have reported successful applications of GA in a variety of research fields (In: Jorg Biethaha J, Nissen V, Evolutionary algorithms in management applications. Berlin: Springer, 1995. p. 44–97; Eur. J. Oper. Res. 80 (1995) 389, Comput. Ind. Eng. 30 (1996) 919). In this study, a new application of GA has been conducted for a combinatorial design problem in statistics — the follow-up design problem. This problem, as with many other statistical combinatorial design problems, has the same characteristic; the elements in a GA solution have strong interactions in calculating the fitness value of the solution. This is quite different from most of the other GA applications. It is believed that this study will create a new research subject in applying search techniques, such as GA, simulated annealing (SA), and tabu search (TS) to statistical combinatorial design problems and other problems having the same property.
    Relation: Computers and Operations Research, 30(2), 232-252
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/S0305-0548(01)00093-4
    DOI: 10.1016/S0305-0548(01)00093-4
    Appears in Collections:[資訊管理學系] 期刊論文

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