Hyper-optimization with Gaussian Process and Differential Evolution Algorithm

TL;DR

This paper introduces modifications to Gaussian Process-based Bayesian optimization, enhancing performance in high-demand problems, demonstrated through success in the BlackBox 2020 challenge.

Contribution

It presents specific modifications to Gaussian Process optimization components, improving efficiency and effectiveness in high computational demand scenarios.

Findings

Outperformed some conventional optimization libraries in BlackBox 2020 challenge

Modified Gaussian Process components improved optimization performance

Demonstrated effectiveness in high computational demand problems

Abstract

Optimization of problems with high computational power demands is a challenging task. A probabilistic approach to such optimization called Bayesian optimization lowers performance demands by solving mathematically simpler model of the problem. Selected approach, Gaussian Process, models problem using a mixture of Gaussian functions. This paper presents specific modifications of Gaussian Process optimization components from available scientific libraries. Presented modifications were submitted to BlackBox 2020 challenge, where it outperformed some conventionally available optimization libraries.