Publication Cover
Journal of Quality Technology
A Quarterly Journal of Methods, Applications and Related Topics
Volume 35, 2003 - Issue 1
114
Views
67
CrossRef citations to date
0
Altmetric
Articles

Genetic Algorithms for the Construction of D-Optimal Designs

, , , &
Pages 28-46 | Published online: 16 Feb 2018

References

  • Alander, J. T. (1999). “Population Size, Building Blocks, Fitness Landscape and Genetic Algorithm Search Efficiency in Combinatorial Optimization: An Empirical Study”. Practical Handbook of Genetic Algorithms–Complex Coding Systems Vol III. CRC Press, Boca Raton, FL.
  • Ansari, N. and Hon, E. (1997). Computational Intelligence for Optimization. Kluwer Academic Publishers, Norwell, MA.
  • Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, NY.
  • Bierwith, C. and Malfeld, D. C. (1999). “Production Scheduling and Rescheduling with Genetic Algorithms”. Evolutionary Computation 7, pp. 1–17.
  • Box, G. E. P.; Hunter, W. G.; and Hunter, J. S. (1978). Statistics for Experimenters. John Wiley & Sons, New York, NY.
  • Cook, R. D. and Nachtsheim, C. J. (1980). “A Comparison of Algorithms for Constructing Exact D-Optimal Designs”. Technometrics 22, pp. 315–324.
  • Donev, A. N. and Atkinson, A. C. (1988). “An Adjustment Algorithm for the Construction of Exact D-Optimum Experimental Designs”. Technometrics 30, pp. 429–433.
  • Dykstra, O. (1971). “The Augmentation of Experimental Data to Maximize |X′X|”. Technometrics 13, pp. 682–688.
  • Evans, J.W. (1979). “Computer Augmentation of Experimental Designs to Maximize |X′X|”. Technometrics 21, pp. 321–330.
  • Falkenauer, E. (1998). Genetic Algorithms and Grouping Problems. John Wiley & Sons, West Sussex, Great Britain.
  • Govaerts, B. and Sanchez Rubal, P. (1992). “Construction of Exact D-optimal Designs for Linear Regression Models Using Genetic Algorithms”. Belgian Journal of Operations Research, Statistics and Computer Science 1–2, pp. 153–174.
  • Haines, L. M. (1987). “The Application of the Annealing Algorithm to the Construction of Exact Optimal Designs for Linear-Regression Models”. Technometrics 29, pp. 439–447.
  • Hamada, M.; Martz, H. F.; Reese, C. S.; and Wilson, A. G. (2001). “Finding Near-Optimal Bayesian Experimental Designs Via Genetic Algorithms”. The American Statistician 55, pp. 175–181.
  • Hancock, P. J. B. (1995). “Selection Methods for Evolutionary Algorithms”. Practical Handbook of Genetic Algorithms. New Frontiers Vol. II. CRC Press, Boca Raton, FL.
  • Holland (1975). Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA.
  • Johnson, M. E. and Nachtsheim, C. J. (1983). “Some Guidelines for Constructing Exact D-Optimal Designs on Convex Design Spaces”. Technometrics 25, pp. 271–277.
  • Koza, J. R. (1993). “Hierarchical Automatic Function Definition in Genetic Programming” Foundations of Genetic Algorithms 2. Morgan Kauffman Publishers, San Mateo, CA.
  • Meyer, R. K. and Nachtsheim, C. J. (1995). “The Coordinate-Exchange Algorithm for Constructing Exact Optimal Experimental Designs”. Technometrics 37, pp. 60–69.
  • Mitchell, T. J. (1974). “An Algorithm for the Construction of ‘D-Optimal’ Experimental Designs”. Technometrics 20, pp. 203–210.
  • Montepiedra, G.; Myers, D.; and Yeh, A. B. (1998). “Application of Genetic Algorithms to the Construction of Exact D-Optimal Designs”. Journal of Applied Statistics 6, pp. 817–826.
  • Montgomery, D. C. (2001). Design and Analysis of Experiments 5th ed. John Wiley & Sons Inc. New York, NY.
  • Myers, R. H. and Montgomery, D. C. (1995). Response Surface Methodology. John Wiley & Sons Inc. New York, NY.
  • Rasmussen, S. and Barrett, C. L. (1995). “Elements of a Theory of Simulation.” European Conference on Artificial Life, Proceedings. Springer, Berlin, Germany.
  • Scott, S. D., Seth, S., and Samal, A. (1999). “A Synthesizable VHDL coding of a Genetic Algorithm”. Practical Handbook of Genetic Algorithms - Complex Coding Systems Vol III. CRC Press, Boca Raton, FL.
  • Snee, R. D. and Marquardt, D. W. (1974). “Extreme Vertices Designs for Linear Mixture Models”. Technometrics 16, pp. 399–408.
  • Snee, R. D. (1985). “Computer-Aided Design of Experiments — Some Practical Experiences”. Journal of Quality Technology 17, pp. 222–236.
  • St. John, R. C. and Draper, N. R. (1975). “D-Optimality for Regression Designs: A Review”. Technometrics 17, pp. 15–23.
  • Vainio, M., Schnberg, T., Halme, A., and Jakubik, P. (1995). “Optimizing the Performance of a Robot Society in Structured Environments through Genetic Algorithms”. European Conference on Artificial Life, Proceedings. Springer, Berlin, Germany.
  • Vuchkov, I. N.; Damgaliev, D. L.; and Yontchev, Ch. A. (1981). “Sequentially Generated Second Order Quasi D-Optimal Designs for Experiments with Mixture and Process Variables”. Technometrics 23, pp. 233–238.
  • Welch, W. J. (1982). “Branch and Bound Search for Experimental Designs Based on D Optimality and Other Criteria”. Technometrics 24, pp. 41–48.
  • Welch, W. J. (1984). “Computer-Aided Design of Experiments for Response Estimation”. Technometrics 26, pp. 217–224.
  • Wynn, H. P. (1970). “The Sequential Generation of D-optimum Experimental Designs”. Annals of Mathematical Statistics 41, pp. 1655–1664.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.