Global optimization of deceptive functions with sparse sampling

Description/Abstract

This paper introduces a new method of calculating the expected improvement infill
criterion, which does not rely on accurate model parameter estimation. The parameter
estimation is embedded within the search of the infill criterion, wherein parameter changes
are assessed using likelihood ratio tests. Unlike the traditional expected improvement, a
new formulation we present cannot be `fooled' by unlucky sampling or deceptive functions.
The new method is introduced both mathematically and illustratively using a one-variable
test function. It is then shown to outperform traditional expected improvement when
optimizing the geometry of a passive vibration isolating truss.