Adaptive experimental design applied to an ergonomics testing procedure

Michael Sasena, Matthew Parkinson, Pierre Goovaerts, Panos Papalambros, Matthew Reed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch and Jones) that is able to create adaptive designs effectively. Its ability to allow easily for a variety of sampling criteria and to incorporate constraint information accurately makes it well suited to the needs of adaptive design. The approach is demonstrated on a human reach experiment where the selection of sampling points adapts successfully to the stature and perception of the individual test subject. Results from the initial study indicate that superEGO is able to create experimental designs that yield more accurate models using fewer points than the original testing procedure.

Original languageEnglish (US)
Title of host publicationProceedings of the ASME Design Engineering Technical Conference
PublisherAmerican Society of Mechanical Engineers
Number of pages9
ISBN (Electronic)0791836223
StatePublished - 2002
Event28th Design Automation Conference - Montreal, Que., Canada
Duration: Sep 29 2002Oct 2 2002

Publication series

NameProceedings of the ASME Design Engineering Technical Conference


Conference28th Design Automation Conference
CityMontreal, Que.

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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