Polynomial genetic programming for response surface modeling Part 2: adaptive approximate models with probabilistic optimization problems
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- @Article{yeun_2005_SMO2,
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author = "Y. S. Yeun and B. J. Kim and Y. S. Yang and
W. S. Ruy",
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title = "Polynomial genetic programming for response surface
modeling Part 2: adaptive approximate models with
probabilistic optimization problems",
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journal = "Structural and Multidisciplinary Optimization",
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year = "2005",
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volume = "29",
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number = "1",
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pages = "35--49",
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month = jan,
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keywords = "genetic algorithms, genetic programming, partial
interpolation strategy, polynomial genetic programming,
reliability-based optimization, response surface
method",
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DOI = "doi:10.1007/s00158-004-0461-5",
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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