Browse > Article
http://dx.doi.org/10.5351/CKSS.2007.14.3.531

Hybrid Approach When Multiple Objectives Exist  

Kim, Young-Il (Department of Information System, ChungAng University)
Lim, Yong-Bin (Department of Statistics, Ewha Women's University)
Publication Information
Communications for Statistical Applications and Methods / v.14, no.3, 2007 , pp. 531-540 More about this Journal
Abstract
When multiple objectives exist, there are three approaches exist. These are maximin design, compound design, and constrained design. Still, each of three design criteria has its own strength and weakness. In this paper Hybrid approach is suggested when multiple design objectives exist, which is a combination of maximin and constrained design. Sometimes experimenter has several objectives, but he/she has only one or two primary objectives, others less important. A new approach should be useful under this condition. The genetic algorithm is used for few examples. It has been proven to be a very useful technique for this complex situation. Conclusion follows.
Keywords
Optimal design; criteria; compound design; maximin design; constrained design; hybrid approach; genetic algorithm;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Cook, R. D. and Fedorov, V. V. (1995). Constrained optimization of experimental design (with discussion). Statistics, 26, 129-178   DOI   ScienceOn
2 Stigler, S. M. (1971). Optimal experimental design for polynomial regression. Journal of the American Statistical Association, 66, 311-318   DOI
3 Cook, R. D. and Wong, W. K. (1994). On the equivalence between constrained and compound optimal designs. Journal of the American Statistical Association, 89, 687-692   DOI
4 Fedorov, V. V. (1972). Theory of Optimal Experiments. Translated and edited by W. J. Studden and E. M. Klimko, Academic Press, New York
5 Kiefer, J. (1959). Optimum experimental design. Journal of the Royal Statistical Society, Ser. B, 21, 272-319
6 Huang, Y. C. (1996). Multiple-objective optimal designs. Doctor of Public Health Dissertation, Department of Biostatistics, School of Public Health, UCLA
7 Huang, Y. C. and Wong, W. K. (1998). Multiple-objective designs. Journal of Biopharmaceutical Statistics, 8, 635-643   DOI   ScienceOn
8 Imhof, L. and Wong, W. K. (2000). A graphical method for finding maximin designs. Biometrics, 56, 113-117   DOI   ScienceOn
9 Lauter, E. (1974). Experimental planning in a class of models. Mathematishe Operationsforshung und Statistik, 5, 673-708
10 Park, Y. J., Montgomery, D. C., Folwer, J. W. and Borror, C. M. (2005). Costconstrained G-efficient response surface designs for cuboidal regions. Quality and Reliability Engineering International, 22, 121-139   DOI   ScienceOn
11 Pukelsheim, F. (1993). Optimal Design of Experiments. John Wiley & Sons, New York. Silvey, S. D. (1980). Optimal Design. Chapman & Hall/CRC
12 Wong, W. K. (1995). A graphical approach for constructing constrained D- and Loptimal designs using efficiency plot. Journal of Statistical Simulation and Computations, 53, 143-152   DOI   ScienceOn
13 염준근, 남기성 (2000), A study on D-optimal design using the genetic algorithm. 한국통계학회논문집, 7, 357-366
14 Wong, W. K. (1999). Recent advances in multiple-objective design strategies. Statistica Neerlandica, 53, 257-276   DOI
15 강명욱, 김영일 (2002), Multiple constrained optimal experimental design. 한국통계학회논문집, 9, 619-627   과학기술학회마을   DOI
16 강명욱, 김영일 (2006), Strategical issues in multiple-objective optimal expeerimental design. 한국통계학회논문집, 13, 1-10   과학기술학회마을   DOI
17 Studden, W. J. (1982). Some robust type D-optimal designs in polynomial regression. Journal of the American Statistical Association, 77, 916-921   DOI
18 Box, G. E. P. and Draper, N. R. (1975). A basis for the selection of a response surface design. Journal of the American Statistical Association, 54, 622-654   DOI