Developing High Fidelity Approximations to Expensive Simulation Models for Expedited Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Deschaine:2003:informs,

• author = "Larry Deschaine and Janos D. Pinter and Sudip Regmi",
• title = "Developing High Fidelity Approximations to Expensive Simulation Models for Expedited Optimization",
• booktitle = "INFORMS Annual Meeting Conference",
• year = "2003",
• editor = "Donna Llewellyn",
• address = "Atlanta, Georgia, USA",
• month = oct # " 19-22",
• note = "Presented at",
• keywords = "genetic algorithms, genetic programming",
• broken = "https://informs.emeetingsonline.com/emeetings/formbuilder/clustersessiondtl.asp?csnno=1278",
• 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.",
• 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