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Evolutionary Optimization of Yagi-Uda AntennasYagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.
Document ID
20020005131
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Lohn, Jason D. (NASA Ames Research Center Moffett Field, CA United States)
Kraus, William F. (NASA Ames Research Center Moffett Field, CA United States)
Linden, Derek S. (Linden Innovation Research Ashburn, VA United States)
Colombano, Silvano P. (NASA Ames Research Center Moffett Field, CA United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2001
Subject Category
Electronics And Electrical Engineering
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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