Testing of Python Models of Parallelized Genetic Algorithms

Skorpil, Vladislav; Oujezský, Václav; Tuleja, Martin

doi:10.1109/tsp49548.2020.9163475

Cited by 4 publications

(8 citation statements)

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“…In a multi-user environment, proper load balancing among processors would increase the efficiency of both the GA and the system. This type of GA gains efficiency when we have a larger number of processors and a population of a constant size [ 4 ].…”

Section: The State-of-the-artmentioning

confidence: 99%

“…The Coarse-Grained model was described in [ 4 ]. The main difference between Fine-Grained and Coarse-Grained genetic algorithms is that the Fine-Grained genetic algorithm works with one global population that is spatially dispersed into a large number of nodes, so it is fine-grained.…”

Section: The State-of-the-artmentioning

confidence: 99%

“…Python modules for implementing GA parallelization within Python and their detailed comparison were given in [ 4 ]. For clarity, the following is a brief summary of the selected modules.…”

Section: Python Modules and Proposal Of The Communication Design Usin...mentioning

confidence: 99%

“…This paper was based on [ 1 ], and its goal was to describe our research in the area of the parallelization of genetic algorithms and the subsequent implementation. Some parts of our research have been gradually published in conference proceedings [ 2 , 3 , 4 ], and this journal article is a significantly extended version (approximately 70%) of the conference proceedings article [ 5 ]. Here, the latest findings and completion of the whole issue are described.…”

Section: Introductionmentioning

confidence: 99%

“…This paper completes our research on parallel genetic algorithms’ implementation using the scalable current operation in Python. We give new details not published in [ 2 , 3 , 4 , 5 ]. The source code supporting this paper is available at [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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

Skorpil

Oujezský

2022

Sensors

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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.

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Section: The State-of-the-artmentioning

confidence: 99%

Section: The State-of-the-artmentioning

confidence: 99%

“…Python modules for implementing GA parallelization within Python and their detailed comparison were given in [ 4 ]. For clarity, the following is a brief summary of the selected modules.…”

Section: Python Modules and Proposal Of The Communication Design Usin...mentioning

confidence: 99%