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Adaptive population techniques in Evolution Algorithms

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F19%3A00006778" target="_blank" >RIV/46747885:24210/19:00006778 - isvavai.cz</a>

  • Result on the web

    <a href="http://mme2019.ef.jcu.cz/files/conference_proceedings.pdf" target="_blank" >http://mme2019.ef.jcu.cz/files/conference_proceedings.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Adaptive population techniques in Evolution Algorithms

  • Original language description

    Evolution algorithms are one of the most popular optimization techniques for its ability to solve wide field of general combinatorial problems. They are simple to implement and use as its mechanisms are logical and untestable due to its natural and common sense. However its optimization results as well as timespan are heavy dependent on setting searching parameters as crossover, mutation rates, population size etc. Setting those parameters requires intuition and experience and is also beneficial to change them during optimization as necessity to exploit and explore changes during optimization. Those are the reasons why nowadays research in the field of Evolutionary Computing is focusing more and more on the adaptive operator control. This article is focusing on part of adaptive parameter control which is known not only as responsible for quality of results but also for optimization time. The research is focusing on population sizing schemes together with selection and elimination procedures. It is reviewing known techniques and presenting original solution. Those techniques are then tested on known theoretical job shop scheduling problems and their efficiency is discussed. This paper was written with aid of students (Vignesh Babu Kuduva Gopinath and Jiří Bareš).

  • 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

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    37th International Conference on Mathematical Methods in Economics 2019

  • ISBN

    978-80-7394-760-6

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    67-72

  • Publisher name

  • Place of publication

    České Budějovice

  • Event location

    České Budějovice

  • Event date

    Jan 1, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000507570400010