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Generating experimental designs involving control and noise variables using genetic algorithms
Author(s) -
Rodriguez Myrta,
Montgomery Douglas C.,
Borror Connie M.
Publication year - 2009
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1020
Subject(s) - construct (python library) , variance (accounting) , computer science , genetic algorithm , noise (video) , algorithm , design of experiments , process (computing) , mathematical optimization , function (biology) , value (mathematics) , control (management) , mathematics , statistics , artificial intelligence , machine learning , accounting , evolutionary biology , business , image (mathematics) , biology , programming language , operating system
Efficient estimation of response variables in a process is an important problem that requires experimental designs appropriated for each specific situation. When we have a system involving control and noise variables, we are often interested in the simultaneous optimization of the prediction variance of the mean (PVM) and the prediction variance of the slope (PVS). The goal of this simultaneous optimization is to construct designs that will result in the efficient estimation of important parameters. We construct new computer‐generated designs using a desirability function by transforming PVM and PVS into one desirability value that can be optimized using a genetic algorithm. Fraction of design space (FDS) plots are used to evaluate the new designs and six cases are discussed to illustrate the procedure. Copyright © 2009 John Wiley & Sons, Ltd.