An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms ; volume:13 ; number:1 ; day:20 ; month:2 ; year:2020 ; pages:223-233 ; date:1.2020
International journal of computational intelligence systems ; 13, Heft 1 (20.2.2020), 223-233, 1.2020
- Creator
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Maghawry, Ahmed
Kholief, Mohamed
Omar, Yasser
Hodhod, Rania
- Contributor
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SpringerLink (Online service)
- DOI
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10.2991/ijcis.d.200214.001
- URN
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urn:nbn:de:101:1-2024041316445525010021
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:01 AM CEST
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Associated
- Maghawry, Ahmed
- Kholief, Mohamed
- Omar, Yasser
- Hodhod, Rania
- SpringerLink (Online service)
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Corrigendum to “An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms” [International Journal of Computational Intelligence Systems Vol. 13(1), 2020, pp. 223–233]
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Corrigendum to “An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms” [International Journal of Computational Intelligence Systems Vol. 13(1), 2020, pp. 223–233]
Detecting recombination in evolving nucleotide sequences
Sampling algorithms for evolving datasets
Evolving distributed algorithms with genetic programming
Fast-evolving noncoding sequences in the human genome
Evolving blackbox quantum algorithms using genetic programming
Hybrid evolutionary algorithms
Towards resolving Lamiales relationships: insights from rapidly evolving chloroplast sequences
Uncovering deeply conserved motif combinations in rapidly evolving noncoding sequences
Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
Supply chain transformation : evolving with emerging business paradigms
A hybrid model for drawing dynamic and evolving graphs
Corrigendum to “An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms” [International Journal of Computational Intelligence Systems Vol. 13(1), 2020, pp. 223–233]
Detecting recombination in evolving nucleotide sequences
Sampling algorithms for evolving datasets
Evolving distributed algorithms with genetic programming
Fast-evolving noncoding sequences in the human genome
Evolving blackbox quantum algorithms using genetic programming
Hybrid evolutionary algorithms
Towards resolving Lamiales relationships: insights from rapidly evolving chloroplast sequences
Uncovering deeply conserved motif combinations in rapidly evolving noncoding sequences
Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
Supply chain transformation : evolving with emerging business paradigms