Model Guided Sampling Optimization with Gaussian Processes for Expensive Black-Box Optimization

Abstract

Model Guided Sampling Optimization (MGSO) is a novel expensive black-box optimization method based on a combination of ideas from Estimation of Distribution Algorithms and global optimization methods using Gaussian Processes. The algorithm is described and its implementation tested on three benchmark functions as a proof of concept.

Keywords

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