Developing High Fidelity Approximations to Expensive Simulation Models for Expedited Optimization
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- @InProceedings{Deschaine:2003:informs,
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author = "Larry Deschaine and Janos D. Pinter and Sudip Regmi",
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title = "Developing High Fidelity Approximations to Expensive
Simulation Models for Expedited Optimization",
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booktitle = "INFORMS Annual Meeting Conference",
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year = "2003",
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editor = "Donna Llewellyn",
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address = "Atlanta, Georgia, USA",
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month = oct # " 19-22",
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note = "Presented at",
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keywords = "genetic algorithms, genetic programming",
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broken = "https://informs.emeetingsonline.com/emeetings/formbuilder/clustersessiondtl.asp?csnno=1278",
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abstract = "Integrated simulation and optimisation typically
requires a sequence of 'expensive' function calls.
While extremely valuable in concept, when the
computation cost of simulations functions is high
(hours / days) and or the optimization paradigm is
inefficient (thousands of function calls), real-time or
timely 'optimal' solutions are elusive. We discuss the
use of machine learning to develop a high fidelity
model of a process simulator that executes quickly
(milliseconds). This function is then optimised using
the LGO solver, thus enabling optimisation in
real-time.",
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notes = "https://www.informs.org/Meetings-Conferences/INFORMS-Conference-Calendar/Past-Events/INFORMS-Annual-Meeting-Atlanta-2003
",
- }
Genetic Programming entries for
Larry M Deschaine
Janos D Pinter
Sudip Regmi
Citations