Genetic algorithms: theory, genetic operators, solutions, and applications
A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills t...
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| Veröffentlicht in: | Evolutionary intelligence Jg. 17; H. 3; S. 1245 - 1256 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2024
Springer Nature B.V |
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| ISSN: | 1864-5909, 1864-5917 |
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| Abstract | A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills the population with random candidate solutions and develops the optimal solution from one generation to the next. The GA applies a set of genetic operators during the search process: selection, crossover, and mutation. This article aims to review and summarize the recent contributions to the GA research field. In addition, the definitions of the GA essential concepts are reviewed. Furthermore, the article surveys the real-life applications and roles of GA. Finally, future directions are provided to develop the field. |
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| AbstractList | A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills the population with random candidate solutions and develops the optimal solution from one generation to the next. The GA applies a set of genetic operators during the search process: selection, crossover, and mutation. This article aims to review and summarize the recent contributions to the GA research field. In addition, the definitions of the GA essential concepts are reviewed. Furthermore, the article surveys the real-life applications and roles of GA. Finally, future directions are provided to develop the field. |
| Author | Awajan, Arafat Alhijawi, Bushra |
| Author_xml | – sequence: 1 givenname: Bushra orcidid: 0000-0003-0806-102X surname: Alhijawi fullname: Alhijawi, Bushra email: b.alhijawi@psut.edu.jo organization: Princess Sumaya University for Technology – sequence: 2 givenname: Arafat surname: Awajan fullname: Awajan, Arafat organization: Princess Sumaya University for Technology, Mutah University |
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| Keywords | GA Evolutionary algorithm Optimization Genetic algorithms Genetic operators |
| Language | English |
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| PublicationPlace | Berlin/Heidelberg |
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| PublicationTitle | Evolutionary intelligence |
| PublicationTitleAbbrev | Evol. Intel |
| PublicationYear | 2024 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
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