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Advanced Genetic Algorithms for Engineering Design Problems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F11%3APU91547" target="_blank" >RIV/00216305:26210/11:PU91547 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Advanced Genetic Algorithms for Engineering Design Problems

  • Original language description

    The study of analogy of the natural evolution and the technical object design dates back more than 50 years. The genetic algorithm (GA) is considered to be a stochastic heuristic (or meta-heuristic) optimisation method. The best use of GA can be found insolving multidimensional optimisation problems, for which analytical solutions are unknown (or extremely complex) and efficient numerical methods are also not known. Genetic algorithms are inspired by adaptive and evolutionary mechanisms of live organisms, but they do not copy the natural process precisely. The paper describes the main terms, principles and original implementation details of GA. The main goal of this paper is to help readers to use proper GAs on the field of technical objects design.

  • 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

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

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

    Engineering Mechanics

  • ISSN

    1802-1484

  • e-ISSN

  • Volume of the periodical

    17

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    11

  • Pages from-to

  • UT code for WoS article

  • EID of the result in the Scopus database