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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

  • Czech description

Classification

  • 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)

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

  • 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

  • Event date

    Jul 10, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000773791800063