Improving the Model Guided Sampling Optimization by Model Search and Slice Sampling

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.

Keywords

• Benchmarking
• Black-box optimization
• Modeling
• Gaussian Processes