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Proceedings Paper

Endgame implementations for the Efficient Global Optimization (EGO) algorithm
Author(s): Hugh L. Southall; Teresa H. O'Donnell; Bryan Kaanta
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Paper Abstract

Efficient Global Optimization (EGO) is a competent evolutionary algorithm which can be useful for problems with expensive cost functions [1,2,3,4,5]. The goal is to find the global minimum using as few function evaluations as possible. Our research indicates that EGO requires far fewer evaluations than genetic algorithms (GAs). However, both algorithms do not always drill down to the absolute minimum, therefore the addition of a final local search technique is indicated. In this paper, we introduce three "endgame" techniques. The techniques can improve optimization efficiency (fewer cost function evaluations) and, if required, they can provide very accurate estimates of the global minimum. We also report results using a different cost function than the one previously used [2,3].

Paper Details

Date Published: 29 April 2009
PDF: 13 pages
Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470Q (29 April 2009); doi: 10.1117/12.820460
Show Author Affiliations
Hugh L. Southall, Air Force Research Lab. (United States)
Visitronix Inc. onsite Consultant (United States)
Teresa H. O'Donnell, Air Force Research Lab. (United States)
ARCON Corp. onsite Consultant (United States)
Bryan Kaanta, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 7347:
Evolutionary and Bio-Inspired Computation: Theory and Applications III
Teresa H. O'Donnell; Misty Blowers; Kevin L. Priddy, Editor(s)

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