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Restarted Local Search Algorithms for Continuous Black Box Optimization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00199146" target="_blank" >RIV/68407700:21230/12:00199146 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00087" target="_blank" >http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00087</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Restarted Local Search Algorithms for Continuous Black Box Optimization

  • Original language description

    Several local search algorithms for real valued domains (axis parallel line search, Nelder Mead simplex search, Rosenbrock's algorithm, quasi Newton method, NEWUOA, and VXQR) are described and thoroughly compared in this article, embedding them in a multi start method. Their comparison aims (1) to help the researchers from the evolutionary community to choose the right opponent for their algorithm (to choose an opponent that would constitute a hard to beat baseline algorithm), (2) to describe individualfeatures of these algorithms and show how they influence the algorithm on different problems, and (3) to provide inspiration for the hybridization of evolutionary algorithms with these local optimizers. The recently proposed Comparing Continuous Optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that in low dimensional spaces, the old method of Nelder and Mead is still the most successful among those compared, while in spaces of higher dimensions

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GP102%2F08%2FP094" target="_blank" >GP102/08/P094: Machine learning methods for solution construction in evolutionary algorithms</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2012

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Evolutionary Computation

  • ISSN

    1063-6560

  • e-ISSN

  • Volume of the periodical

    20

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    33

  • Pages from-to

    575-607

  • UT code for WoS article

    000311334400005

  • EID of the result in the Scopus database