Počet záznamů: 1  

Combining Gaussian Processes with Neural Networks for Active Learning in Optimization

  1. 1.
    SYSNO ASEP0555604
    Druh ASEPC - Konferenční příspěvek (mezinárodní konf.)
    Zařazení RIVD - Článek ve sborníku
    NázevCombining Gaussian Processes with Neural Networks for Active Learning in Optimization
    Tvůrce(i) Růžička, J. (CZ)
    Koza, J. (CZ)
    Tumpach, J. (CZ)
    Pitra, Z. (CZ)
    Holeňa, Martin (UIVT-O) SAI, RID
    Celkový počet autorů5
    Zdroj.dok.IAL@ECML PKDD 2021: Workshop on Interactive Adaptive Learning. Proceedings. - Aachen : Technical University & CreateSpace Independent Publishing, 2021 / Krempl G. ; Lemaire V. ; Kottke D. ; Holzinger A. ; Hammer B. - ISSN 1613-0073
    Rozsah strans. 105-120
    Poč.str.16 s.
    Forma vydáníTištěná - P
    AkceIAL 2021: Workshop on Interactive Adaptive Learning /5./
    Datum konání13.09.2021 - 13.09.2021
    Místo konáníBilbao / virtual
    ZeměES - Španělsko
    Typ akceWRD
    Jazyk dok.eng - angličtina
    Země vyd.DE - Německo
    Klíč. slovaactive learning ; black-box optimization ; artificial neural networks ; Gaussian processes ; covariance functions
    Vědní obor RIVIN - Informatika
    Obor OECDComputer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    CEPGA18-18080S GA ČR - Grantová agentura ČR
    Institucionální podporaUIVT-O - RVO:67985807
    EID SCOPUS85124088084
    AnotaceOne area where active learning plays an important role is black-box optimization of objective functions with expensive evaluations. To deal with such evaluations, continuous black-box optimization has adopted an approach called surrogate modelling or metamodelling, which consists in replacing the true black-box objective in some of its evaluations with a suitable regression model, the selection of evaluations for replacement being an active learning task. This paper concerns surrogate modelling in the context of a surrogate-assisted variant of the continuous black-box optimizer Covariance Matrix Adaptation Evolution Strategy. It reports the experimental investigation of surrogate models combining artificial neural networks with Gaussian processes, for which it considers six different covariance functions. The experiments were performed on the set of 24 noiseless benchmark functions of the platform Comparing Continuous Optimizers COCO with 5 different dimensionalities. Their results revealed that the most suitable covariance function for this combined kind of surrogate models is the rational quadratic followed by the Matérn 25 and squared exponential. Moreover, the rational quadratic and squared exponential covariances were found interchangeable in the sense that for no function, no group of functions, no dimension and combination of them, the performance of the respective surrogate models was significantly different.
    PracovištěÚstav informatiky
    KontaktTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Rok sběru2022
    Elektronická adresahttp://ceur-ws.org/Vol-3079/ial2021_paper9.pdf
Počet záznamů: 1  

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