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Regular Grammar Transformation Inspired by the Graph Distance Using GA

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F11%3A00183348" target="_blank" >RIV/68407700:21260/11:00183348 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.nnw.cz" target="_blank" >http://www.nnw.cz</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Regular Grammar Transformation Inspired by the Graph Distance Using GA

  • Original language description

    This paper introduces a method how to transform one regular grammar to the second one. The transformation is based on regular grammar distance computation. Regular grammars are equivalent to finite states machines and they are represented by oriented graphs or by transition matrices, respectively. Thus, the regular grammar distance is defined analogously to the distance between two graphs. The distance is measured as the minimal count of elementary operations over the grammar which transform the first grammar to the second one. The distance is computed by searching an optimal mapping of non-terminal symbols of both grammars. The computation itself is done by the genetic algorithm because the exhaustive evaluation of mapping leads to combinatorial explosion. Transformation steps are derived from differences in matrices. Differences are identified during the computation of the distance.

  • Czech name

  • Czech description

Classification

  • Type

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

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2011

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    11

  • Pages from-to

    299-309

  • UT code for WoS article

    000295384200002

  • EID of the result in the Scopus database