Developing High Fidelity Approximations to Expensive Simulation Models for Expedited Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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",
  address =      "Atlanta, Georgia, USA",
  month =        oct # " 19-22",
  note =         "Presented at",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "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 =        "

                 ",
}

Genetic Programming entries for Larry M Deschain Janos D Pinter Sudip Regmi

Citations