Hyper-optimization with Gaussian Process and Differential Evolution Algorithm
This work addresses optimization challenges for researchers and practitioners in fields requiring high computational power, but it is incremental as it builds on existing methods.
The paper tackled the problem of optimizing computationally demanding tasks by modifying Gaussian Process components, and the result was that their approach outperformed some conventional optimization libraries in the BlackBox 2020 challenge.
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.