Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F17%3A43919251" target="_blank" >RIV/00023752:_____/17:43919251 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/68407700:21340/17:00317541
Výsledek na webu
<a href="http://ceur-ws.org/Vol-1885/120.pdf" target="_blank" >http://ceur-ws.org/Vol-1885/120.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy
Popis výsledku v původním jazyce
An area of increasingly frequent applications of evolutionary optimization to real-world problems is continuous black-box optimization. However, evaluating realworld black-box fitness functions is sometimes very timeconsuming or expensive, which interferes with the need of evolutionary algorithms for many fitness evaluations. Therefore, surrogate regression models replacing the original expensive fitness in some of the evaluated points have been in use since the early 2000s. The Doubly Trained Surrogate Covariance Matrix Adaptation Evolution strategy (DTS-CMA-ES) represents a surrogate-assisted version of the state-of-the-art algorithm for continuous blackbox optimization CMA-ES. The DTS-CMA-ES saves expensive function evaluations through using a surrogate model. However, the model inaccuracy on some functions can slow-down the algorithm convergence. This paper investigates an extension of DTS-CMA-ES which controls the usage of the model according to the model’s error. Results of testing an adaptive and the original version of DTS-CMA-ES on the set of noiseless benchmarks are reported
Název v anglickém jazyce
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy
Popis výsledku anglicky
An area of increasingly frequent applications of evolutionary optimization to real-world problems is continuous black-box optimization. However, evaluating realworld black-box fitness functions is sometimes very timeconsuming or expensive, which interferes with the need of evolutionary algorithms for many fitness evaluations. Therefore, surrogate regression models replacing the original expensive fitness in some of the evaluated points have been in use since the early 2000s. The Doubly Trained Surrogate Covariance Matrix Adaptation Evolution strategy (DTS-CMA-ES) represents a surrogate-assisted version of the state-of-the-art algorithm for continuous blackbox optimization CMA-ES. The DTS-CMA-ES saves expensive function evaluations through using a surrogate model. However, the model inaccuracy on some functions can slow-down the algorithm convergence. This paper investigates an extension of DTS-CMA-ES which controls the usage of the model according to the model’s error. Results of testing an adaptive and the original version of DTS-CMA-ES on the set of noiseless benchmarks are reported
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
<a href="/cs/project/LO1611" target="_blank" >LO1611: Udržitelnost pro Národní ústav duševního zdraví</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2017
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
17th Conference on Information Technologies - Applications and Theory, ITAT 2017; Hotel Martinske HoleMartinske Hole; Slovakia; 22 September 2017 through 26 September 2017
ISBN
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ISSN
1613-0073
e-ISSN
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Počet stran výsledku
9
Strana od-do
120-128
Název nakladatele
CEUR Workshop Proceedings
Místo vydání
Slovensko
Místo konání akce
Slovensko
Datum konání akce
22. 9. 2017
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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