Heuristic optimization of experimental designs

Heuristic optimization of experimental designs

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Article ID: iaor20042427
Country: Netherlands
Volume: 147
Issue: 3
Start Page Number: 484
End Page Number: 498
Publication Date: Jun 2003
Journal: European Journal of Operational Research
Authors:
Keywords: heuristics, optimization: simulated annealing
Abstract:

We propose to integrate different algorithms for constructing D-optimum designs for linear models. Our emphasis is on efficiency gain and on applicability to larger models than those currently considered in the literature. We implement a one-exchange algorithm and use a generalized simulated annealing. This method does not require to construct or to enumerate each point of the candidate set, whose size grows exponentially with the number of variables. In order to handle more complex problems, we develop a procedure generating guided starting designs. A comparison of our results with those found in the literature shows that the simultaneous integration of these algorithms turns out to be very effective. As compared to results from the literature, our algorithmic process allows an increase in efficiency while, for larger models (up to 20 parameters), we attain a 90% D-efficiency level.

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