Combining Gaussian processes and neural networks in surrogate modeling for covariance matrix adaptation evolution strategy
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10450902" target="_blank" >RIV/00216208:11320/21:10450902 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/67985807:_____/21:00546157
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Combining Gaussian processes and neural networks in surrogate modeling for covariance matrix adaptation evolution strategy
Popis výsledku v původním jazyce
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 state-of-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.
Název v anglickém jazyce
Combining Gaussian processes and neural networks in surrogate modeling for covariance matrix adaptation evolution strategy
Popis výsledku anglicky
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 state-of-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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
CEUR Workshop Proceedings
ISBN
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ISSN
1613-0073
e-ISSN
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Počet stran výsledku
10
Strana od-do
29-38
Název nakladatele
CEUR-WS
Místo vydání
Neuveden
Místo konání akce
Muránska planina, Slovakia
Datum konání akce
24. 9. 2021
Typ akce podle státní příslušnosti
EUR - Evropská akce
Kód UT WoS článku
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