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

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Article name in the collection

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

ISBN

978-1-4503-8350-9

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

528-536

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Lille / Online

Event date

Jul 10, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000773791800063