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Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration.

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F15%3APU117070" target="_blank" >RIV/00216305:26230/15:PU117070 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.fit.vut.cz/research/publication/11013/" target="_blank" >https://www.fit.vut.cz/research/publication/11013/</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Elliptical and Archimedean Copulas in Estimation of Distribution Algorithm with Model Migration.

  • Original language description

    Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that are based on building and sampling a probability model. Copula theory provides methods that simplify the estimation of a probability model. An island-based version of copula-based EDA with probabilistic model migration (mCEDA) was tested on a set of well-known standard optimization benchmarks in the continuous domain. We investigated two families of copulas - Archimedean and elliptical. Experimental results confirm that this concept of model migration (mCEDA) yields better convergence as compared with the sequential version (sCEDA) and other recently published copula-based EDAs.

  • 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

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2015

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015)

  • ISBN

    978-989-758-157-1

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    212-219

  • Publisher name

    SciTePress - Science and Technology Publications

  • Place of publication

    Lisbon

  • Event location

    Lisbon

  • Event date

    Nov 12, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article