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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Vydané v:	Applied soft computing Ročník 104; s. 107198
Hlavní autori:	Sambo, Aliyu Sani, Azad, R. Muhammad Atif, Kovalchuk, Yevgeniya, Indramohan, Vivek Padmanaabhan, Shah, Hanifa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.06.2021
Predmet:	Evaluation time Genetic programming Model complexity Parallel computing Parallel computing Model complexity Genetic programming Evaluation time
ISSN:	1568-4946, 1872-9681
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