Parallel Genetic Algorithms' Implementation Using a Scalable Concurrent Operation in Python

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144347" target="_blank" >RIV/00216305:26220/22:PU144347 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/22/6/2389" target="_blank" >https://www.mdpi.com/1424-8220/22/6/2389</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s22062389" target="_blank" >10.3390/s22062389</a>

Result language
angličtina
Original language name
Parallel Genetic Algorithms' Implementation Using a Scalable Concurrent Operation in Python
Original language description
This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master-Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results' implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering

Project
<a href="/en/project/VI20192022135" target="_blank" >VI20192022135: Deep hardware detection of network traffic of next generation passive optical network in critical infrastructures</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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