Publication detail

Comparison of Models of Parallelized Genetic Algorithms

ŠKORPIL, V. OUJEZSKÝ, V. TULEJA, M.

Original Title

Comparison of Models of Parallelized Genetic Algorithms

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

ŠKORPIL, V.; OUJEZSKÝ, V.; TULEJA, M.

Released

30. 10. 2019

Publisher

IEEE

Location

Dublin, Irsko

ISBN

978-1-7281-5763-4

Book

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

ISBN

2157-023X

Periodical

International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

State

unknown

Pages from

1

Pages to

5

Pages count

5

URL

https://ieeexplore.ieee.org/document/8970944

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"
}