Parallel Processing of Genetic Algorithms in Python Language

Skorpil, Vladislav; Oujezský, Václav; Číka, Petr; Tuleja, Martin

doi:10.1109/piers-spring46901.2019.9017332

Cited by 5 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Fine-Grained model works with one global population spatially dispersed into nodes, creating thus a topology with neighborhoods. The closest environment of the node gives the neighborhood, and the neighborhoods may overlap [ 2 ]. On the other hand, due to the relative isolation of a neighborhood, the best individuals do not spread as fast as in other types of GAs, which increases the population diversity.…”

Section: The State-of-the-artmentioning

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%

See 2 more Smart Citations

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

Skorpil

Oujezský

2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: The State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Skorpil

Oujezský

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…This middleware transforms a command line for running or accessing the various IoT module features.In order to increase program operating time performance, Python employs multiprocessing or multithreading.V. Skorpil et al,[36] The paper discussed various methods for parallelizing genetic algorithms with subsequent implementation. For example purposes, the Python programming language is used.…”

mentioning

confidence: 99%

Python Parallel Processing and Multiprocessing: A Rivew

et al. 2021

View full text Add to dashboard Cite

Parallel and multiprocessing algorithms break down significant numerical problems into smaller subtasks, reducing the total computing time on multiprocessor and multicore computers. Parallel programming is well supported in proven programming languages such as C and Python, which are well suited to “heavy-duty” computational tasks. Historically, Python has been regarded as a strong supporter of parallel programming due to the global interpreter lock (GIL). However, times have changed. Parallel programming in Python is supported by the creation of a diverse set of libraries and packages. This review focused on Python libraries that support parallel processing and multiprocessing, intending to accelerate computation in various fields, including multimedia, attack detection, supercomputers, and genetic algorithms. Furthermore, we discussed some Python libraries that can be used for this purpose.

show abstract

Parallel and Distributed Computing Approaches for Evolutionary Algorithms—A Review

Raghul

Jeyakumar

2021

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Parallel Processing of Genetic Algorithms in Python Language

Cited by 5 publications

References 4 publications

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

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

Python Parallel Processing and Multiprocessing: A Rivew

Parallel and Distributed Computing Approaches for Evolutionary Algorithms—A Review

Contact Info

Product

Resources

About