Polynomial genetic programming for response surface modeling Part 2: adaptive approximate models with probabilistic optimization problems

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@Article{yeun_2005_SMO2,
  author =       "Y. S. Yeun and B. J. Kim and Y. S. Yang and 
                 W. S. Ruy",
  title =        "Polynomial genetic programming for response surface
                 modeling Part 2: adaptive approximate models with
                 probabilistic optimization problems",
  journal =      "Structural and Multidisciplinary Optimization",
  year =         "2005",
  volume =       "29",
  number =       "1",
  pages =        "35--49",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, partial
                 interpolation strategy, polynomial genetic programming,
                 reliability-based optimization, response surface
                 method",
  URL =          "http://www.springerlink.com/app/home/contribution.asp?wasp=pggkrvwxwq5hf4uqva6q&referrer=parent&backto=issue,3,6;journal,2,60;linkingpublicationresults,1:102504,1;",
  DOI =          "doi:10.1007/s00158-004-0461-5",
  abstract =     "This is the second in a series of papers. The first
                 deals with polynomial genetic programming (PGP)
                 adopting the directional derivative-based smoothing
                 (DDBS) method, while in this paper, an adaptive
                 approximate model (AAM) based on PGP is presented with
                 the partial interpolation strategy (PIS). The AAM is
                 sequentially modified in such a way that the quality of
                 fitting in the region of interest where an optimum
                 point may exist can be gradually enhanced, and
                 accordingly the size of the learning set is gradually
                 enlarged. If the AAM uses a smooth high-order
                 polynomial with an interpolative capability, it becomes
                 more and more difficult for PGP to obtain smooth
                 polynomials, whose size should be larger than or equal
                 to the number of the samples, because the order of the
                 polynomial becomes unnecessarily high according to the
                 increase in its size. The PIS can avoid this problem by
                 selecting samples belonging to the region of interest
                 and interpolating only those samples. Other samples are
                 treated as elements of the extended data set (EDS).
                 Also, the PGP system adopts a multiple-population
                 approach in order to simultaneously handle several
                 constraints. The PGP system with the variable-fidelity
                 response surface method is applied to reliability-based
                 optimization (RBO) problems in order to significantly
                 cut the high computational cost of RBO. The AAMs based
                 on PGP are responsible for fitting probabilistic
                 constraints and the cost function while the
                 variable-fidelity response surface method is
                 responsible for fitting limit state equations. Three
                 numerical examples are presented to show the
                 performance of the AAM based on PGP.",
}

Genetic Programming entries for Yun Seog Yeun B J Kim Young-Soon Yang Won-Sun Ruy

Citations