Evolving simple and accurate symbolic regression models via asynchronous parallel computing

In machine learning, reducing the complexity of a model can help to improve its computational efficiency and avoid overfitting. In genetic programming (GP), the model complexity reduction is often achieved by reducing the size of evolved expressions. However, previous studies have demonstrated that...

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Veröffentlicht in:	Applied soft computing Jg. 104; S. 107198
Hauptverfasser:	Sambo, Aliyu Sani, Azad, R. Muhammad Atif, Kovalchuk, Yevgeniya, Indramohan, Vivek Padmanaabhan, Shah, Hanifa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.06.2021
Schlagworte:	Evaluation time Genetic programming Model complexity Parallel computing Parallel computing Model complexity Genetic programming Evaluation time
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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