Detail publikace

Comparison of Models of Parallelized Genetic Algorithms

Originální název

Comparison of Models of Parallelized Genetic Algorithms

Anglický název

Comparison of Models of Parallelized Genetic Algorithms

Jazyk

en

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.

Anglický 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.

BibTex


@inproceedings{BUT159752,
  author="Vladislav {Škorpil} and Václav {Oujezský} and Martin {Tuleja}",
  title="Comparison of Models of Parallelized Genetic Algorithms",
  annote="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.",
  address="IEEE",
  booktitle="Proceedings of the 10th IEEE International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT 2019)",
  chapter="159752",
  howpublished="electronic, physical medium",
  institution="IEEE",
  year="2019",
  month="october",
  pages="1--5",
  publisher="IEEE",
  type="conference paper"
}