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Symbolic regression and evolutionary computation in setting an optimal trajectory for a robot

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F07%3A63505865" target="_blank" >RIV/70883521:28140/07:63505865 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Symbolic regression and evolutionary computation in setting an optimal trajectory for a robot

  • Original language description

    The paper deals with a novelty tool for symbolic regression - Analytic Programming (AP) which is able to solve various problems from the symbolic regression domain. One of tasks for it can be setting an optimal trajectory for artificial ant on Santa Fe trail which is the main application of Analytic Pro-gramming in this paper. In this contribution main principles of AP are de-scribed and explained. In second part of the article how AP was used for setting an optimal trajectory for artificial ant according the user requirements is in de-tail described. An ability to create so called programms, as well as Genetic Pro-gramming (GP) or Grammatical Evolution (GE) do, is shown in that part. AP is a superstructure of evolutionary algorithms which are necessary to run AP. In this contribution 3 evolutionary algorithms were used - Self Organizing Mi-grating Algorithm, Differential Evolution and Simulated Annealing. The results show that the first two used algorithms were more successful than no

  • Czech name

    Symbolická regrese a evoluční výpočty při nastavení optimální trajektorie robota

  • Czech description

    Článek se zabývá novou metodou pro symbolickou regresi - Analytickým programováním (AP), který dokáže řešit různé problémy z oblasti symbolické regrese. Jedním z úkolů může být nastavení optimální trajektorie pro umělého mravence na stezce Santa FE. Kromě principů AP je zde osvětlené, jak AP bylo použito na nastavení trajektorie pro umělého mravence. Byly zde použity 3 evoluční algoritmy jako optimalizační nástroje - SOMA, DE a simulované žíhání (SA). Výsledky ukázaly, že je nutné zvolit vhodný optimalizační nástroj, protože simulované žíhání neobstálo tak dobře jako SOMA a DE.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

  • Project

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

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2007

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    workshop ETID 2007 in DEXA 2007

  • ISBN

    978-0-7695-2932-5

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    168-172

  • Publisher name

    IEEE Computer Society

  • Place of publication

    Německo

  • Event location

  • Event date

  • Type of event by nationality

  • UT code for WoS article