Towards Landscape Analysis in Adaptive Learning of Surrogate Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00536612" target="_blank" >RIV/67985807:_____/20:00536612 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2660/ialatecml_shortpaper2.pdf" target="_blank" >http://ceur-ws.org/Vol-2660/ialatecml_shortpaper2.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Towards Landscape Analysis in Adaptive Learning of Surrogate Models

Popis výsledku v původním jazyce

A context in which we expect adaptive learning to be promising is the choice of a suitable optimization strategy in black-box optimization. The reason why strategy adaptation is needed in such a situation is that knowledge of the blackbox objective function is obtained only gradually during the optimization. That knowledge covers two aspects: 1. the landscape of the black-box objective, revealed through its evaluation in previous iterations./ 2. success or failure of the optimization strategies applied to that black-box objective in previous iterations. To extract landscape knowledge, landscape analysis has been developed during the last decade. To include also the second aspect, we complement features obtained using the landscape analysis with features describing the optimization employed in previous iterations. Our interest is in expensive black-box optimization, where the number of evaluations of the expensive objective is usually decreased using a suitable surrogate model. Therefore, the research reported in this extended abstract addresses adaptive learning of surrogate models, more precisely their learning in surrogateassisted versions of the state-of-the-art black-box optimization method, Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

Název v anglickém jazyce

Towards Landscape Analysis in Adaptive Learning of Surrogate Models

Popis výsledku anglicky

A context in which we expect adaptive learning to be promising is the choice of a suitable optimization strategy in black-box optimization. The reason why strategy adaptation is needed in such a situation is that knowledge of the blackbox objective function is obtained only gradually during the optimization. That knowledge covers two aspects: 1. the landscape of the black-box objective, revealed through its evaluation in previous iterations./ 2. success or failure of the optimization strategies applied to that black-box objective in previous iterations. To extract landscape knowledge, landscape analysis has been developed during the last decade. To include also the second aspect, we complement features obtained using the landscape analysis with features describing the optimization employed in previous iterations. Our interest is in expensive black-box optimization, where the number of evaluations of the expensive objective is usually decreased using a suitable surrogate model. Therefore, the research reported in this extended abstract addresses adaptive learning of surrogate models, more precisely their learning in surrogateassisted versions of the state-of-the-art black-box optimization method, Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Workshop on Interactive Adaptive Learning

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

6

Strana od-do

78-83

Název nakladatele

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Virtual Ghent

Datum konání akce

14. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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