Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00557942" target="_blank" >RIV/67985807:_____/21:00557942 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/21:00354467 RIV/68407700:21340/21:00354467 RIV/00216208:11320/21:10450955

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3449639.3459358" target="_blank" >http://dx.doi.org/10.1145/3449639.3459358</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3449639.3459358" target="_blank" >10.1145/3449639.3459358</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy

Popis výsledku v původním jazyce

Surrogate regression models have been shown as a valuable technique in evolutionary optimization to save evaluations of expensive black-box objective functions. Each surrogate modelling method has two complementary components: the employed model and the control of when to evaluate the model and when the true objective function, aka evolution control. They are often tightly interconnected, which causes difficulties in understanding the impact of each component on the algorithm performance. To contribute to such understanding, we analyse what constitutes the evolution control of three surrogate-assisted versions of the state-of-the-art algorithm for continuous black-box optimization --- the Covariance Matrix Adaptation Evolution Strategy. We implement and empirically compare all possible combinations of the regression models employed in those methods with the three evolution controls encountered in them. An experimental investigation of all those combinations allowed us to asses the influence of the models and their evolution control separately. The experiments are performed on the noiseless and noisy benchmarks of the Comparing-Continuous-Optimisers platform and a real-world simulation benchmark, all in the expensive scenario, where only a small budget of evaluations is available.

Název v anglickém jazyce

Interaction between Model and its Evolution Control in Surrogate-assisted CMA Evolution Strategy

Popis výsledku anglicky

Surrogate regression models have been shown as a valuable technique in evolutionary optimization to save evaluations of expensive black-box objective functions. Each surrogate modelling method has two complementary components: the employed model and the control of when to evaluate the model and when the true objective function, aka evolution control. They are often tightly interconnected, which causes difficulties in understanding the impact of each component on the algorithm performance. To contribute to such understanding, we analyse what constitutes the evolution control of three surrogate-assisted versions of the state-of-the-art algorithm for continuous black-box optimization --- the Covariance Matrix Adaptation Evolution Strategy. We implement and empirically compare all possible combinations of the regression models employed in those methods with the three evolution controls encountered in them. An experimental investigation of all those combinations allowed us to asses the influence of the models and their evolution control separately. The experiments are performed on the noiseless and noisy benchmarks of the Comparing-Continuous-Optimisers platform and a real-world simulation benchmark, all in the expensive scenario, where only a small budget of evaluations is available.

Druh

D - Stať ve sborníku

CEP obor

—

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings Of The 2021 Genetic And Evolutionary Computation Conference (Gecco'21)

ISBN

978-1-4503-8350-9

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

528-536

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Lille / Online

Datum konání akce

10. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000773791800063