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Meta-Learning and Model Selection in Multiobjective Evolutionary Algorithms

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10126821" target="_blank" >RIV/00216208:11320/12:10126821 - isvavai.cz</a>

  • Alternative codes found

    RIV/67985807:_____/12:00384809

  • Result on the web

    <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6406701" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6406701</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICMLA.2012.78" target="_blank" >10.1109/ICMLA.2012.78</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Meta-Learning and Model Selection in Multiobjective Evolutionary Algorithms

  • Original language description

    Most existing surrogate based evolutionary algorithms deal with only one model selected by the authors and different models are not considered. In this paper we propose a framework which enables automatic selection of types of surrogate models, and evaluate the effect of the type of selection on the overall performance of the resulting evolutionary algorithm. Two different types of model selection are tested and compared both in pre-selection scenario and in local search scenario.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2012

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2012 11th International Conference on Machine Learning and Applications (ICMLA)

  • ISBN

    978-1-4673-4651-1

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    433-438

  • Publisher name

    IEEE Computer Press

  • Place of publication

    Boca Raton, FL

  • Event location

    Boca Raton, FL, USA

  • Event date

    Dec 12, 2012

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article