Application of genetic programming and artificial neural networks to improve engineering optimization
Created by W.Langdon from
gpbibliography.bib Revision:1.6970
 @PhdThesis{Gongtao_Wang:thesis,

author = "Gongtao Wang",

title = "Application of genetic programming and artificial
neural networks to improve engineering optimization",

school = "Lamar University",

year = "1998",

type = "Doctor of Engineering",

address = "Texas, USA",

month = dec,

keywords = "genetic algorithms, genetic programming",

URL = "http://search.proquest.com/docview/304558089",

size = "105 pages",

abstract = "The mathematical models of many engineering problems
are very complex and computationally intensive. These
complex models are repeatedly used to solve problems.
Each optimization process is almost equally burdensome.
One solution is to use a response surface model (RSM)
to simulate the computationally burdensome model.
Several researchers have tried to use conventional
regression to simplify computationally intensive
optimization models. In most reported efforts of this
kind, a quadratic RSM is created from the data
collected from previous operations of the
computationally intensive model. Optimization is then
performed on this simplified model rather than on the
complex one. The original model is then consulted at
the proposed optimum to verify that all constraints are
satisfied. There are two fundamental problems with this
approach. The first is that these methods will be
inherently inaccurate whenever the underlying function
is not quadratic. The second is that it can not recall
what was learned about the shape of the design space
after an optimization is completed. This research will
combine Genetic Programming with Neural Networks to
create an RSM. A new way to perform regression is
described. This method can discover the underlying
simple functional form of a computationally intensive
optimization model over the entire design space. A more
accurate regression model will be built by using this
proper functional form. This RSM can then be saved from
one optimization run to the next to serve as a memory
of the global and local shape of the design space. This
field study develops a new method, which merges Genetic
Programming and Neural Networks into an integrated
system to perform regression. Experiments are then
carried out to compare the existing methods with the
developed method when used for optimization. The
experimental data shows that the new method is
effective if the optimization model is computationally
intensive and a set of historical data is available. If
the two conditions are satisfied and the same optimum
is reached, the computational efficiency improved
94.5percent by using the RSM obtained with the
developed method, as opposed to optimizing the original
model. Compared to using a quadratic RSM, the
efficiency is improved 76percent, as the same optimum
is reached.",

notes = "Supervisor: Victor Zaloom
UMI Microform 9938783",
 }
Genetic Programming entries for
Gongtao Wang
Citations