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Proceedings Paper

Applying EGO to large dimensional optimizations: a wideband fragmented patch example
Author(s): Teresa H. O'Donnell; Hugh Southall; Scott Santarelli; Hans Steyskal
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Paper Abstract

Efficient Global Optimization (EGO) minimizes expensive cost function evaluations by correlating evaluated parameter sets and respective solutions to model the optimization space. For optimizations requiring destructive testing or lengthy simulations, this computational overhead represents a desirable tradeoff. However, the inspection of the predictor space to determine the next evaluation point can be a time-intensive operation. Although DACE predictor evaluation may be conducted for limited parameters by exhaustive sampling, this method is not extendable to large dimensions. We apply EGO here to the 11-dimensional optimization of a wide-band fragmented patch antenna and present an alternative genetic algorithm approach for selecting the next evaluation point. We compare results achieved with EGO on this optimization problem to previous results achieved with a genetic algorithm.

Paper Details

Date Published: 15 April 2010
PDF: 11 pages
Proc. SPIE 7704, Evolutionary and Bio-Inspired Computation: Theory and Applications IV, 770407 (15 April 2010); doi: 10.1117/12.851793
Show Author Affiliations
Teresa H. O'Donnell, Air Force Research Lab. (United States)
Hugh Southall, Air Force Research Lab. (United States)
Scott Santarelli, Air Force Research Lab. (United States)
Hans Steyskal, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 7704:
Evolutionary and Bio-Inspired Computation: Theory and Applications IV
Teresa H. O'Donnell; Misty Blowers; Kevin L. Priddy, Editor(s)

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