Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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://phdtree.org/pdf/25254058-application-of-genetic-programming-and-artificial-neural-networks-to-improve-engineering-optimization/", 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