A review on genetic algorithm : past, present, and future
© Springer Science+Business Media, LLC, part of Springer Nature 2020..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:80 |
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| Enthalten in: |
Multimedia tools and applications - 80(2021), 5 vom: 01., Seite 8091-8126 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Katoch, Sourabh [VerfasserIn] |
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| Links: |
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| Themen: |
Crossover |
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| Anmerkungen: |
Date Revised 18.02.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s11042-020-10139-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM317305549 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Crossover | |
| 650 | 4 | |a Evolution | |
| 650 | 4 | |a Genetic algorithm | |
| 650 | 4 | |a Metaheuristic | |
| 650 | 4 | |a Mutation | |
| 650 | 4 | |a Optimization | |
| 650 | 4 | |a Selection | |
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| 700 | 1 | |a Kumar, Vijay |e verfasserin |4 aut | |
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