A study of gene expression programming algorithm for dynamically adjusting the parameters of genetic operators

The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. Its restrictions block GEP from successfully handl...

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Veröffentlicht in:	PloS one Jg. 20; H. 6; S. e0321711
Hauptverfasser:	Liu, Kejia, Teng, Yiping, Liu, Fang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 02.06.2025 Public Library of Science (PLoS)
Schlagworte:	Ablation Accuracy Algorithms Analysis Artificial Intelligence Benchmarks Biology and Life Sciences Ecology and Environmental Sciences Efficiency Evolutionary algorithms Exploitation Gene Expression Genetic algorithms Genetic engineering Humans Models, Genetic Mutation Operators Optimization Pareto optimum Physical Sciences Population genetics Programming Research and Analysis Methods Searching China
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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