An Efficient Federated Genetic Programming Framework for Symbolic Regression
Download
Full text for this resource is not available from the Research Repository.
Export
Dong, Junlan, Zhong, Jinghui ORCID: 0000-0003-0113-3430, Chen, Wei-Neng ORCID: 0000-0003-0843-5802 and Zhang, Jun ORCID: 0000-0003-4148-4294 (2023) An Efficient Federated Genetic Programming Framework for Symbolic Regression. IEEE Transactions on Emerging Topics in Computational Intelligence, 7 (3). pp. 858-871. ISSN 2471-285X
Dimensions Badge
Altmetric Badge
Item type | Article |
URI | https://vuir.vu.edu.au/id/eprint/47157 |
DOI | 10.1109/TETCI.2022.3201299 |
Official URL | https://ieeexplore.ieee.org/document/9881543 |
Subjects | Current > FOR (2020) Classification > 4602 Artificial intelligence Current > Division/Research > Institute for Sustainable Industries and Liveable Cities |
Keywords | symbolic regression problem; genetic programming algorithms; mean shift aggregation; data privacy; data protection; federated learning; self-learning gene expression programming |
Download/View statistics | View download statistics for this item |
CORE (COnnecting REpositories)