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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00546157" target="_blank" >RIV/67985807:_____/21:00546157 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/21:10450902

Výsledek na webu

<a href="http://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper27.pdf" target="_blank" >http://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper27.pdf</a>

DOI - Digital Object Identifier

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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 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.

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 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.

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)

Projekt

<a href="/cs/project/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021)

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

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Heľpa

Datum konání akce

24. 9. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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