Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy

0546157 - ÚI 2022 RIV DE eng C - Conference Paper (international conference)
Koza, J. - Tumpach, J. - Pitra, Z. - Holeňa, Martin
Combining Gaussian Processes and Neural Networks in Surrogate Modeling for Covariance Matrix Adaptation Evolution Strategy.
Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021). Aachen: Technical University & CreateSpace Independent Publishing, 2021 - (Brejová, B.; Ciencialová, L.; Holeňa, M.; Mráz, F.; Pardubská, D.; Plátek, M.; Vinař, T.), s. 29-38. ISSN 1613-0073.
[ITAT 2021: Information Technologies - Applications and Theory /21./. Heľpa (SK), 24.09.2021-28.09.2021]
R&D Projects: GA ČR(CZ) GA18-18080S
Grant - others:Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
Institutional support: RVO:67985807
Keywords : black-box optimization * surrogate modeling * artificial neural networks * Gaussian processes * covariance functions
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper27.pdf

This paper focuses on surrogate models for Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in continuous black-box optimization. Surrogate modeling has proven to be able to decrease the number of evaluations of the objective function, which is an important requirement in some real-world applications where the evaluation can be costly or time-demanding. Surrogate models achieve this by providing an approximation instead of the evaluation of the true objective function. One of the stateof-the-art models for this task is the Gaussian process. We present an approach to combining Gaussian processes with artificial neural networks, which was previously successfully applied to other machine learning domains. The experimental part employs data recorded from previous CMA-ES runs, allowing us to assess different settings of surrogate models without running the whole CMA-ES algorithm. The data were collected using 24 noiseless benchmark functions of the platform for comparing continuous optimizers COCO in 5 different dimensions. Overall, we used data samples from over 2.8 million generations of CMA-ES runs. The results examine and statistically compare six covariance functions of Gaussian processes with the neural network extension. So far, the combined model did not show up to outperform the Gaussian process alone. Therefore, in conclusion, we discuss possible reasons for this and ideas for future research.
Permanent Link: http://hdl.handle.net/11104/0322706