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29 April 2009 Endgame implementations for the Efficient Global Optimization (EGO) algorithm
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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].
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hugh L. Southall, Teresa H. O'Donnell, and Bryan Kaanta "Endgame implementations for the Efficient Global Optimization (EGO) algorithm", Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470Q (29 April 2009);

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