Heuristic and metaheuristic algorithms for the generation of optimal experimental designs
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This is a summary of the author’s Ph.D. thesis, supervised by Peter Goos and Kenneth Sörensen, and defended on June 15th, 2015 at the Universiteit Antwerpen, Belgium. The thesis is written in English and is available from the author upon request at email@example.com. The topic of this Ph.D. lies in the intersection between operations research and statistics: it proposes new algorithmic techniques for the generation of optimal experimental designs.
The purpose of an experiment is to identify the influence that a set of experimental variables has on the process under study. By systematically manipulating the settings of these variables, it is possible to quantify how and to which extent they affect one or more response variables that measure the process’s behaviour. The design of an experiment mainly consists in determining the number of experimental runs, the settings of the experimental variables in each run, and the sequence in which the runs need to be executed. This...