Instance based Transfer Learning for Genetic Programming for Symbolic Regression

被引:0
|
作者
Chen, Qi [1 ]
Xue, Bing [1 ]
Zhang, Mengjie [1 ]
机构
[1] Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington, New Zealand
关键词
Genetic Programming; Transfer learning; Symbolic Regression; CLASSIFICATION;
D O I
10.1109/cec.2019.8790217
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Transfer learning aims to utilise 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 utilise 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.
引用
收藏
页码:3006 / 3013
页数:8
相关论文
共 50 条