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ŠKORPIL, V. OUJEZSKÝ, V. ČÍKA, P. TULEJA, M.
Original Title
Parallel Processing of Genetic Algorithms in Python Language
Type
conference paper
Language
English
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.
Keywords
genetic algorithm; parallel processing; model; Python
Authors
ŠKORPIL, V.; OUJEZSKÝ, V.; ČÍKA, P.; TULEJA, M.
Released
17. 6. 2019
Publisher
IEEE
Location
Rome, Italy
ISBN
978-4-88552-316-8
Book
2019 Progress in Electomagnetics Research Symposium (PIERS - Rome)
1559-9450
Periodical
Progress In Electromagnetics
State
United States of America
Pages from
3727
Pages to
3731
Pages count
5
URL
https://ieeexplore.ieee.org/abstract/document/9017332
BibTex
@inproceedings{BUT159755, author="Vladislav {Škorpil} and Václav {Oujezský} and Petr {Číka} and Martin {Tuleja}", title="Parallel Processing of Genetic Algorithms in Python Language", booktitle="2019 Progress in Electomagnetics Research Symposium (PIERS - Rome)", year="2019", journal="Progress In Electromagnetics", pages="3727--3731", publisher="IEEE", address="Rome, Italy", doi="10.1109/PIERS-Spring46901.2019.9017332", isbn="978-4-88552-316-8", issn="1559-9450", url="https://ieeexplore.ieee.org/abstract/document/9017332" }