Hyper-optimization with Gaussian Process and Differential Evolution Algorithm

• URL: http://arxiv.org/abs/2101.10625v1
• Date: Tue, 26 Jan 2021 08:33:00 GMT
• Title: Hyper-optimization with Gaussian Process and Differential Evolution Algorithm
• Authors: Jakub Klus, Pavel Grunt, Martin Dobrovoln\'y
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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