@inproceedings{631f34f3664149d392874c9c9cfe6a64,
title = "Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm",
abstract = "The Rapid Gaussian Markov Improvement Algorithm (rGMIA) solves discrete optimization via simulation problems by using a Gaussian Markov random field and complete expected improvement as the sampling and stopping criterion. rGMIA has been created as a sequential sampling procedure run on a single processor. In this paper, we extend rGMIA to a parallel computing environment when q + 1 solutions can be simulated in parallel. To this end, we introduce the q-point complete expected improvement criterion to determine a batch of q + 1 solutions to simulate. This new criterion is implemented in a new object-oriented rGMIA package.",
author = "Mark Semelhago and Nelson, {Barry L.} and Eunhye Song and Andreas Wachter",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Winter Simulation Conference, WSC 2022 ; Conference date: 11-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/WSC57314.2022.10015429",
language = "English (US)",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3158--3169",
editor = "B. Feng and G. Pedrielli and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and E.P. Chew and T. Roeder and P. Lendermann",
booktitle = "Proceedings of the 2022 Winter Simulation Conference, WSC 2022",
address = "United States",
}