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The Journal of Supercomputing

Erschienen in:

04.02.2023

A decentralized method for initial populations of genetic algorithms

verfasst von: Reza Roshani, Homayon Motameni, Hosein Mohamadi

Erschienen in: The Journal of Supercomputing | Ausgabe 9/2023

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Abstract

Today, evolutionary algorithms are widely used in a variety of fields for problem solving and optimization purposes. The genetic algorithms (GA) entail a number of primary stages which have been optimized many times to date; one of these stages involves the generation of the initial population. Generating a suitable and diverse initial population can prevent the early convergence of the problem and greatly contributes to problem-solving abilities and speed. The proposed method in this study involves generating an initial population in a decentralized manner between a number of processing nods which are generated as subpopulations before being integrated. Carrying out this procedure using the conditions present in any processors increases diversity in the population. The presented method is used based on estimating the parameters of software reliability growth model (SRGMs). Finally, it is shown that the proposed model managed to achieve population diversity, enhanced accuracy, and increased performance.

Vorheriger Artikel Dynamic two-side matching of tasks and resources in wide-area distributed computing environments

Nächster Artikel BALANCER: bandwidth allocation and cache partitioning for multicore processors

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Titel: A decentralized method for initial populations of genetic algorithms
verfasst von: Reza Roshani
Homayon Motameni
Hosein Mohamadi
Publikationsdatum: 04.02.2023
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 9/2023
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-023-05066-w

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