Design Optimization based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "L. F. Alvarez",
  title =        "Design Optimization based on Genetic Programming",
  school =       "Department of Civil and Environmental Engineering,
                 University of Bradford",
  year =         "2000",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming, Design
                 Optimization, Response Surface Methodology",
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  abstract =     "This thesis addresses two problems arising in many
                 real-life design optimization applications: the high
                 computational cost of function evaluations and the
                 presence of numerical noise in the function values. The
                 response surface methodology is used to construct
                 approximations of the original model. A major
                 difficulty in building highly accurate response
                 surfaces is the selection of the structure of an
                 approximation function.

                 A methodology has been developed for the approximation
                 model building using genetic programming. It is
                 implemented in a computer code introducing two new
                 features: the use of design sensitivity information
                 when available, and the allocation and evaluation of
                 tuning parameters in separation from the evolutionary
                 process. A combination of a genetic algorithm and a
                 gradient-based algorithm is used for tuning of the
                 approximation functions. The problem of the choice of a
                 design of experiments in the response surface
                 methodology has been reviewed and a space-filling plan

                 The developed methodology and software have been
                 applied to design optimization problems with
                 numerically simulated and experimental responses,
                 demonstrating their considerable potential. The
                 applications cover the approximation of a response
                 function obtained by a finite element model for the
                 detection of damage in steel frames, the creation of an
                 empirical model for the prediction of the shear
                 strength in concrete deep beams and a multicriteria
                 optimization of the process of calcination of Roman
  notes =        "Approximation model building for design optimization
                 using the response surface methodology and genetic

                 Luis Francisco Alvarez Barrioluengo

                 Supervisor V.V. Toropov",

Genetic Programming entries for Luis F Alvarez