Detail publikace

Comparison of Models of Parallelized Genetic Algorithms

ŠKORPIL, V. OUJEZSKÝ, V. TULEJA, M.

Originální název

Comparison of Models of Parallelized Genetic Algorithms

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The aim of the paper is to describe the most widely used methods of parallelization of GA genetic algorithms and subsequently to use the outputs of the theoretical part for the design of implementation. Python was chosen as the implementation language, so the design is implemented with this language in mind. Selected problems of sequential GA are described in the theoretical part of the paper. Optimization problems and parallel models are described. They are the Global One - Population Master-Slave Model, the One-Population Fine-Grained Model, the Multi-Population Coarse-Grained Model, and the Hierarchical Model. The practical part deals with the design and implementation of parallelized GA.

Klíčová slova

genetic algorithm; multiprocessing; model; optimization; parallelization; Python

Autoři

ŠKORPIL, V.; OUJEZSKÝ, V.; TULEJA, M.

Vydáno

30. 10. 2019

Nakladatel

IEEE

Místo

Dublin, Irsko

ISBN

978-1-7281-5763-4

Kniha

Proceedings of the 11th IEEE International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT 2019)

ISSN

2157-023X

Periodikum

International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

Stát

neuvedeno

Strany od

1

Strany do

5

Strany počet

5

URL

BibTex

@inproceedings{BUT159752,
  author="Vladislav {Škorpil} and Václav {Oujezský} and Martin {Tuleja}",
  title="Comparison of Models of Parallelized Genetic Algorithms",
  booktitle="Proceedings of the 11th IEEE International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT 2019)",
  year="2019",
  journal="International Congress on Ultra Modern Telecommunications and Control Systems and Workshops",
  pages="1--5",
  publisher="IEEE",
  address="Dublin, Irsko",
  doi="10.1109/ICUMT48472.2019.8970944",
  isbn="978-1-7281-5763-4",
  issn="2157-023X",
  url="https://ieeexplore.ieee.org/document/8970944"
}