Generator Approach to Evolutionary Optimization of Catalysts and its Integration with Surrogate Modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00355267" target="_blank" >RIV/67985807:_____/11:00355267 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Generator Approach to Evolutionary Optimization of Catalysts and its Integration with Surrogate Modeling

Popis výsledku v původním jazyce

This paper presents some unpublished aspects and ongoing developments of the recently elaborated generator approach to the evolutionary optimization of catalytic materials, the purpose of which is to obtain evolutionary algorithms precisely tailored to the problem being solved. It briefly recalls the principles of the approach, and then it describes how the employed evolutionary operations reflect the specificity of the involved mixed constrained optimization tasks, and how the approach tackles checkingthe feasibility of large polytope systems, frequently resulting from the optimization constraints. Finally, the paper discusses the integration of the approach with surrogate modeling, paying particular attention to surrogate models enhanced with boosting. The usefulness of surrogate modeling in general and of boosted surrogate models in particular is documented on a case study with data from a high-temperature synthesis of hydrocyanic acid.

Název v anglickém jazyce

Generator Approach to Evolutionary Optimization of Catalysts and its Integration with Surrogate Modeling

Popis výsledku anglicky

This paper presents some unpublished aspects and ongoing developments of the recently elaborated generator approach to the evolutionary optimization of catalytic materials, the purpose of which is to obtain evolutionary algorithms precisely tailored to the problem being solved. It briefly recalls the principles of the approach, and then it describes how the employed evolutionary operations reflect the specificity of the involved mixed constrained optimization tasks, and how the approach tackles checkingthe feasibility of large polytope systems, frequently resulting from the optimization constraints. Finally, the paper discusses the integration of the approach with surrogate modeling, paying particular attention to surrogate models enhanced with boosting. The usefulness of surrogate modeling in general and of boosted surrogate models in particular is documented on a case study with data from a high-temperature synthesis of hydrocyanic acid.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

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

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

Catalysis Today

ISSN

0920-5861

e-ISSN

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Svazek periodika

159

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

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Kód UT WoS článku

000285626500011

EID výsledku v databázi Scopus

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