Publication detail

Parallel Processing of Genetic Algorithms in Python Language

ŠKORPIL, V. OUJEZSKÝ, V. ČÍKA, P. TULEJA, M.

Original Title

Parallel Processing of Genetic Algorithms in Python Language

English Title

Parallel Processing of Genetic Algorithms in Python Language

Type

conference paper

Language

en

Original Abstract

Modern genetic algorithms are derived from natural laws and phenomenons and belong to evolutionary algorithms. Genetic algorithms are, by their very nature, suitable for parallel processing that leads to increased speed and to optimization. The paper deals with selected ways of parallelization of genetic algorithms with subsequent implementation. Parallelization brings an increase in algorithm speed and load distribution, which is compared to a serial model. Python language is used for demonstration. Four Python modules have been selected to provide parallel processing. 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.

English abstract

Modern genetic algorithms are derived from natural laws and phenomenons and belong to evolutionary algorithms. Genetic algorithms are, by their very nature, suitable for parallel processing that leads to increased speed and to optimization. The paper deals with selected ways of parallelization of genetic algorithms with subsequent implementation. Parallelization brings an increase in algorithm speed and load distribution, which is compared to a serial model. Python language is used for demonstration. Four Python modules have been selected to provide parallel processing. 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.

Keywords

genetic algorithm; parallel processing; model; Python

Released

17.06.2019

Publisher

IEEE

Location

Rome, Italy

ISBN

978-4-88552-316-8

Book

2019 Progress in Electomagnetics Research Symposium (PIERS - Rome)

Pages from

1

Pages to

5

Pages count

5

Documents

BibTex


@inproceedings{BUT159755,
  author="Vladislav {Škorpil} and Václav {Oujezský} and Petr {Číka} and Martin {Tuleja}",
  title="Parallel Processing of Genetic Algorithms in Python Language",
  annote="Modern genetic algorithms are derived from natural laws and phenomenons and belong to evolutionary algorithms. Genetic algorithms are, by their very nature, suitable for parallel processing that leads to increased speed and to optimization. The paper deals with selected ways of parallelization of genetic algorithms with subsequent implementation. Parallelization brings an increase in algorithm speed and load distribution, which is compared to a serial model. Python language is used for demonstration. Four Python modules have been selected to provide parallel processing. 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.",
  address="IEEE",
  booktitle="2019 Progress in Electomagnetics Research Symposium (PIERS - Rome)",
  chapter="159755",
  howpublished="online",
  institution="IEEE",
  year="2019",
  month="june",
  pages="1--5",
  publisher="IEEE",
  type="conference paper"
}