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Genetic programming applications in electromagnetics
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|Title:||Genetic programming applications in electromagnetics|
|Authors:||Nakatsu, Jill Sachie Kobashigawa|
|Issue Date:||May 2012|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [May 2012]|
|Abstract:||This thesis considers the application of Genetic Programming (GP) to create computer programs that can solve both classification and metamaterial design problems in the area of electromagnetics (EM). Specifically, GP is used to develop an automatic target classification algorithm and is combined with the patterning Lindenmayer system for the development of a metamaterial design program. For both studies, GP is compared to other popular artificial intelligence (AI) techniques in each area such as the Neural Networks (NN) and the Genetic Algorithm (GA) methods. It is shown that Genetic Programming provides improved classification results and when applied to design work leads to unconventional and global optimal solutions.|
In the target classification of buried objects it is desired to develop an accurate and reliable analysis and classification of electromagnetic data for buried unexploded ordnance (UXO) discrimination. The classification of this data is vital to not only clear buried UXO leftover from war and military training areas across the world with minimal false alarm rates but also to provide opportunities to use this land for housing and business development. GP is compared with neural networks, a popular classification technique, for the classification of UXO scattering patterns. Three classification scenarios with various levels of difficulty were examined and in all cases GP outperformed the NNs.
For the metamaterial design study, a GP program was developed that generates novel, efficient, and unintuitive "broadband" metamaterial designs. There has been no established methodology for developing a successful design of ultra wideband and low frequency metamaterial structures and to this end; GP is used to investigate the development of unconventional designs. A metamaterial design system combining GP with Lindenmayer system (L-system) patterning rules was developed and utilized in the study. A Matlab toolbox which controls both the GP algorithm and the full EM wave simulation in HFSS was also developed and utilized in the comparison of the GP-L system to the genetic algorithm. It is shown that GP is indeed capable of developing designs with improved performance from those reported using the GA methods. This thesis includes a detailed description of the developed GP code, fitness function, and obtained results from both studies.
|Description:||M.S. University of Hawaii at Manoa 2012.|
Includes bibliographical references.
|Appears in Collections:||M.S. - Electrical Engineering|
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