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Title: MISO - Mixed Integer Surrogate Optimization

MISO is an optimization framework for solving computationally expensive mixed-integer, black-box, global optimization problems. MISO uses surrogate models to approximate the computationally expensive objective function. Hence, derivative information, which is generally unavailable for black-box simulation objective functions, is not needed. MISO allows the user to choose the initial experimental design strategy, the type of surrogate model, and the sampling strategy.
Publication Date:
OSTI Identifier:
Report Number(s):
MISO; 004617MLTPL00
R&D Project: YN0100000; 2016-014
DOE Contract Number:
Resource Type:
Software Revision:
Software Package Number:
Software CPU:
Source Code Available:
Other Software Info:
LBL chooses to control all distribution for this particular software.
Related Software:
global optimizer toobox
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
Contributing Orgs:
Lawrence Berkeley National Laboratory
Country of Publication:
United States

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