Evolutionary optimization with active learning of surrogate models and fixed evaluation batch size

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F12%3A00200867" target="_blank" >RIV/68407700:21340/12:00200867 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/12:00384885

Výsledek na webu

<a href="http://itat.ics.upjs.sk/Site/Zborniky" target="_blank" >http://itat.ics.upjs.sk/Site/Zborniky</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary optimization with active learning of surrogate models and fixed evaluation batch size

Popis výsledku v původním jazyce

Evolutionary optimization is often applied to problems, where simulations or experiments used as the fit ness function are expensive to run. In such cases, surro gate models are used to reduce the number of fitness eval uations. Some of the problems alsorequire a fixed size batch of solutions to be evaluated at a time. Traditional methods of selecting individuals for true evaluation to im prove the surrogate model either require individual points to be evaluated, or couple the batch size with the EA gener ation size. We propose a queue based method for individual selection based on active learning of a kriging model. Indi viduals are selected using the confidence intervals predicted by the model, added to a queue and evaluated once the queue length reaches the batch size. The method was tested on several standard benchmark problems. Results show that the proposed algorithm is able to achieve a solution using significantly less evaluations of the true fitness function. The effect of th

Název v anglickém jazyce

Evolutionary optimization with active learning of surrogate models and fixed evaluation batch size

Popis výsledku anglicky

Evolutionary optimization is often applied to problems, where simulations or experiments used as the fit ness function are expensive to run. In such cases, surro gate models are used to reduce the number of fitness eval uations. Some of the problems alsorequire a fixed size batch of solutions to be evaluated at a time. Traditional methods of selecting individuals for true evaluation to im prove the surrogate model either require individual points to be evaluated, or couple the batch size with the EA gener ation size. We propose a queue based method for individual selection based on active learning of a kriging model. Indi viduals are selected using the confidence intervals predicted by the model, added to a queue and evaluated once the queue length reaches the batch size. The method was tested on several standard benchmark problems. Results show that the proposed algorithm is able to achieve a solution using significantly less evaluations of the true fitness function. The effect of th

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F08%2F0802" target="_blank" >GA201/08/0802: Aplikace metod znalostního inženýrství při dobývání znalostí z databází</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Conference on Theory and Practice of information Technologies

ISBN

978-80-971144-0-4

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

33-40

Název nakladatele

Univerzita P. J. Šafárika

Místo vydání

Košice

Místo konání akce

Belianské Tatry

Datum konání akce

17. 9. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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