Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00317541" target="_blank" >RIV/68407700:21340/17:00317541 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00023752:_____/17:43919251

Výsledek na webu

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DOI - Digital Object Identifier

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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 real-world black-box fitness functions is sometimes very time-consuming 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 black- box 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 real-world black-box fitness functions is sometimes very time-consuming 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 black- box 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.

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/LO1611" target="_blank" >LO1611: Udržitelnost pro Národní ústav duševního zdraví</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

ITAT 2017: Information Technologies – Applications and Theory

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

9

Strana od-do

120-128

Název nakladatele

CEUR Workshop Proceedings

Místo vydání

Aachen

Místo konání akce

Martinské hole, Malá Fatra

Datum konání akce

22. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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