- author = "Qi Chen and Bing Xue and Mengjie Zhang",
- title = "Instance based Transfer Learning for Genetic Programming for Symbolic Regression",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "3006--3013",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, Transfer learning, Symbolic Regression",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790217",
- size = "8 pages",
- abstract = "Transfer learning aims to use knowledge acquired from the source domain to improve the learning performance in the target domain. It attracts increasing interests and many transfer learning approaches have been proposed. However, studies on transfer learning for genetic programming for symbolic regression are still rare, although clearly desired, due to the difficulty to evolve models with a good cross-domain generalisation ability. This work proposes a new instance weighting framework for transfer learning in genetic programming for symbolic regression. The key idea is to use a local weight updating scheme to identify and learn from more useful source domain instances and reduce the effort on the source domain instances, which are more different from the target domain data. The experimental results show that the proposed method notably enhances the learning capacity and the generalisation performance of genetic programming on the target domain and also outperforms some state-of-the-art regression methods.",
-
notes = "also known as \cite{8790217}
Also https://ecs.wgtn.ac.nz/Groups/ECRG/Talks#26/04/2019
IEEE Catalog Number: CFP19ICE-ART",
