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A review on genetic algorithm: past, present, and future

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algor...

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Detalles Bibliográficos
Autores principales: Katoch, Sourabh, Chauhan, Sumit Singh, Kumar, Vijay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599983/
https://www.ncbi.nlm.nih.gov/pubmed/33162782
http://dx.doi.org/10.1007/s11042-020-10139-6
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author Katoch, Sourabh
Chauhan, Sumit Singh
Kumar, Vijay
author_facet Katoch, Sourabh
Chauhan, Sumit Singh
Kumar, Vijay
author_sort Katoch, Sourabh
collection PubMed
description In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.
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spelling pubmed-75999832020-11-02 A review on genetic algorithm: past, present, and future Katoch, Sourabh Chauhan, Sumit Singh Kumar, Vijay Multimed Tools Appl Article In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching. Springer US 2020-10-31 2021 /pmc/articles/PMC7599983/ /pubmed/33162782 http://dx.doi.org/10.1007/s11042-020-10139-6 Text en © Springer Science+Business Media, LLC, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Katoch, Sourabh
Chauhan, Sumit Singh
Kumar, Vijay
A review on genetic algorithm: past, present, and future
title A review on genetic algorithm: past, present, and future
title_full A review on genetic algorithm: past, present, and future
title_fullStr A review on genetic algorithm: past, present, and future
title_full_unstemmed A review on genetic algorithm: past, present, and future
title_short A review on genetic algorithm: past, present, and future
title_sort review on genetic algorithm: past, present, and future
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599983/
https://www.ncbi.nlm.nih.gov/pubmed/33162782
http://dx.doi.org/10.1007/s11042-020-10139-6
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