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ŠKORPIL, V.; OUJEZSKÝ, V.; TULEJA, M.
Original Title
Comparison of Models of Parallelized Genetic Algorithms
English Title
Type
Paper in proceedings (conference paper)
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.
English abstract
Keywords
genetic algorithm; multiprocessing; model; optimization; parallelization; Python
Key words in English
Authors
RIV year
2020
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)
2157-023X
Periodical
International Congress on Ultra Modern Telecommunications and Workshops
State
United States of America
Pages from
1
Pages to
5
Pages count
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 Workshops", pages="1--5", publisher="IEEE", address="Dublin, Irsko", doi="10.1109/ICUMT48472.2019.8970944", isbn="978-1-7281-5763-4", url="https://ieeexplore.ieee.org/document/8970944" }