Publication at Faculty of Mathematics and Physics |
2014
Abstract
Model Guided Sampling Optimization (MGSO) was recently proposed as an alternative for Jones' Kriging-based EGO algorithm for optimization of expensive black-box functions. Instead of maximizing a chosen criterion (e.g., expected improvement), MGSO samples probability of improvement of the Gaussian process model forming multiple candidates -- a~whole population of suggested solutions.
This paper further develops this algorithm using slice sampling method and continuous local optimization of the Gaussian process model.