Efficient global optimization: Motivation, variations and applications
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Date
2016
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Abstract
A popular optimization method of a black box objective function is
Efficient Global Optimization (EGO), also known as Sequential Model Based
Optimization, SMBO, with kriging and expected improvement. EGO is a sequential
design of experiments aiming at gaining as much information as possible
from as few experiments as feasible by a skillful choice of the factor
settings in a sequential way. In this paper we will introduce the standard procedure
and some of its variants. In particular, we will propose some new variants
like regression as a modeling alternative to kriging and two simple methods for
the handling of categorical variables, and we will discuss focus search for the
optimization of the infill criterion. Finally, we will give relevant examples for
the application of the method. Moreover, in our group, we implemented all the
described methods in the publicly available R package mlrMBO.