Abstract
Adaptive design refers to experimental design where the next sample point is determined by information from previous experiments. This article presents a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch, and Jones) that can create adaptive designs using kriging approximations. Our primary goal is to illustrate that superEGO is well-suited to generating adaptive designs which have many advantages over competing methods. The approach is demonstrated on a novel human-reach experiment where the selection of sampling points adapts to the individual test subject. Results indicate that superEGO is effective at satisfying the experimental objectives.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
