Interaction between model and its evolution control in surrogate-assisted CMA evolution strategy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00354467" target="_blank" >RIV/68407700:21240/21:00354467 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/21:00354467 RIV/67985807:_____/21:00557942 RIV/00216208:11320/21:10450955
Result on the web
<a href="https://doi.org/10.1145/3449639.3459358" target="_blank" >https://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>
Alternative languages
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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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference
ISBN
9781450383509
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
528-536
Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Lille
Event date
Jul 10, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000773791800063