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A comprehensive database of Nature-Inspired Algorithms
These data contain a comprehensive collection of all Nature-Inspired Algorithms. This collection is a result of two corresponding surveys, where all Nature-Inspired Algorithms that have been published to-date were gathered and preliminary data acquired. The rapidly increasing number of nature-inspir...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300069/ https://www.ncbi.nlm.nih.gov/pubmed/32577446 http://dx.doi.org/10.1016/j.dib.2020.105792 |
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author | Tzanetos, Alexandros Fister, Iztok Dounias, Georgios |
author_facet | Tzanetos, Alexandros Fister, Iztok Dounias, Georgios |
author_sort | Tzanetos, Alexandros |
collection | PubMed |
description | These data contain a comprehensive collection of all Nature-Inspired Algorithms. This collection is a result of two corresponding surveys, where all Nature-Inspired Algorithms that have been published to-date were gathered and preliminary data acquired. The rapidly increasing number of nature-inspired approaches makes it hard for interested researchers to keep up. Moreover, a proper taxonomy is necessary, based on specific features of the algorithms. Different taxonomies and useful insight into the application areas that the algorithms have coped with is given through these data. This article provides a detailed description of the above mentioned collection. |
format | Online Article Text |
id | pubmed-7300069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-73000692020-06-22 A comprehensive database of Nature-Inspired Algorithms Tzanetos, Alexandros Fister, Iztok Dounias, Georgios Data Brief Computer Science These data contain a comprehensive collection of all Nature-Inspired Algorithms. This collection is a result of two corresponding surveys, where all Nature-Inspired Algorithms that have been published to-date were gathered and preliminary data acquired. The rapidly increasing number of nature-inspired approaches makes it hard for interested researchers to keep up. Moreover, a proper taxonomy is necessary, based on specific features of the algorithms. Different taxonomies and useful insight into the application areas that the algorithms have coped with is given through these data. This article provides a detailed description of the above mentioned collection. Elsevier 2020-06-02 /pmc/articles/PMC7300069/ /pubmed/32577446 http://dx.doi.org/10.1016/j.dib.2020.105792 Text en © 2020 The Author(s). Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Computer Science Tzanetos, Alexandros Fister, Iztok Dounias, Georgios A comprehensive database of Nature-Inspired Algorithms |
title | A comprehensive database of Nature-Inspired Algorithms |
title_full | A comprehensive database of Nature-Inspired Algorithms |
title_fullStr | A comprehensive database of Nature-Inspired Algorithms |
title_full_unstemmed | A comprehensive database of Nature-Inspired Algorithms |
title_short | A comprehensive database of Nature-Inspired Algorithms |
title_sort | comprehensive database of nature-inspired algorithms |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300069/ https://www.ncbi.nlm.nih.gov/pubmed/32577446 http://dx.doi.org/10.1016/j.dib.2020.105792 |
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