Research Area:  Machine Learning

Abstract:

Transfer learning has attracted more attention in the machine-learning community recently. It aims to improve the learning performance on the domain of interest with the help of the knowledge acquired from a similar domain(s). However, there is only a limited number of research on tackling transfer learning in genetic programming for symbolic regression. This article attempts to fill this gap by proposing a new instance weighting framework for transfer learning in genetic programming-based symbolic regression. In the new framework, differential evolution is employed to search for optimal weights for source-domain instances, which helps genetic programming to identify more useful source-domain instances and learn from them. Meanwhile, a density estimation method is used to provide good starting points to help the search for the optimal weights while discarding some irrelevant or less important source-domain instances before learning regression models. The experimental results show that compared with genetic programming and support vector regression that learn only from the target instances, and learning from a mixture of instances from the source and target domains without any transfer learning component, the proposed method can evolve regression models which not only achieve notably better cross-domain generalization performance in stability but also reduce the trend of overfitting effectively. Meanwhile, these models are generally much simpler than those generated by the other GP methods.

Keywords:  
Task analysis
Genetic programming
Estimation
Multitasking
Machine learning
Data models
Cybernetics

Author(s) Name:  Qi Chen; Bing Xue; Mengjie Zhang

Publisher name:  IEEE

DOI:  10.1109/TCYB.2020.2969689

Volume Information:  Volume: 52

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9007621