TY - JOUR
T1 - Optimal subset selection of stochastic model using statistical hypothesis test
AU - Choi, Seon Han
AU - Kim, Tag Gon
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - This paper proposes an improved algorithm for the optimal subset selection of a stochastic simulation model. The algorithm uses a statistical hypothesis test based on frequentist inference to evaluate the uncertainty about the selection, and it distributes simulation resources to designs for minimizing the uncertainty in each iteration. Several experiments demonstrate the improved performance compared to the other algorithms, and the performance increases significantly as the noise of the model increases. As a result, its high robustness to noise allows the algorithm to efficiently analyze real-world problems.
AB - This paper proposes an improved algorithm for the optimal subset selection of a stochastic simulation model. The algorithm uses a statistical hypothesis test based on frequentist inference to evaluate the uncertainty about the selection, and it distributes simulation resources to designs for minimizing the uncertainty in each iteration. Several experiments demonstrate the improved performance compared to the other algorithms, and the performance increases significantly as the noise of the model increases. As a result, its high robustness to noise allows the algorithm to efficiently analyze real-world problems.
KW - Optimal subset selection
KW - ranking and selection (R&S)
KW - simulation-based optimization (SBO)
KW - statistical hypothesis test
KW - stochastic model
UR - http://www.scopus.com/inward/record.url?scp=85044136802&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2016.2608982
DO - 10.1109/TSMC.2016.2608982
M3 - Article
AN - SCOPUS:85044136802
VL - 48
SP - 557
EP - 564
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
SN - 2168-2216
IS - 4
ER -