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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Bibliographic Details

Published in:	Applied soft computing Vol. 104; p. 107198
Main Authors:	Sambo, Aliyu Sani, Azad, R. Muhammad Atif, Kovalchuk, Yevgeniya, Indramohan, Vivek Padmanaabhan, Shah, Hanifa
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.06.2021
Subjects:	Evaluation time Genetic programming Model complexity Parallel computing Parallel computing Model complexity Genetic programming Evaluation time
ISSN:	1568-4946, 1872-9681
Online Access:	Get full text
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