The Meta-Model Approach for Simulation-based Design Optimization

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

@PhdThesis{Stinstra:thesis,
  author =       "Erwin Diederik Stinstra",
  title =        "The Meta-Model Approach for Simulation-based Design
                 Optimization",
  school =       "Tilburg University",
  year =         "2006",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://center.uvt.nl/staff/hertog/PhDstudents/Stinstra-thesis.pdf",
  URL =          "https://pure.uvt.nl/portal/en/publications/the-metamodel-approach-for-simulationbased-design-optimization%28713f828a-4716-4a19-af00-e6f5e668a535%29.html",
  broken =       "http://arno.uvt.nl/show.cgi?fid=55804",
  size =         "155 pages",
  ISBN =         "90-5668-180-X",
  abstract =     "The design of products and processes makes increasing
                 use of computer simulations for the prediction of its
                 performance. These computer simulations are
                 considerably cheaper than their physical equivalent.
                 Finding the optimal design has therefore become a
                 possibility. One approach for finding the optimal
                 design using computer simulations is the meta-model
                 approach, which approximates the behaviour of the
                 computer simulation outcome using a limited number of
                 time-consuming computer simulations. This thesis
                 contains four main contributions, which are illustrated
                 by industrial cases. First, a method is presented for
                 the construction of an experimental design for computer
                 simulations when the design space is restricted by many
                 (nonlinear) constraints. The second contribution is a
                 new approach for the approximation of the simulation
                 outcome. This approximation method is particularly
                 useful when the simulation model outcome reacts highly
                 nonlinear to its inputs. Third, the meta-model based
                 approach is extended to a robust optimisation
                 framework. Using this framework, many uncertainties can
                 be taken into account, including uncertainty on the
                 simulation model outcome. The fourth main contribution
                 is the extension of the approach for use in integral
                 design of many parts of complex systems.",
  notes =        "In English",
}

Genetic Programming entries for Erwin Stinstra

Citations