Title | ||
---|---|---|
A fast parallel genetic programming framework with adaptively weighted primitives for symbolic regression |
Abstract | ||
---|---|---|
Genetic programming (GP) is a popular and powerful optimization algorithm that has a wide range of applications, such as time series prediction, classification, data mining, and knowledge discovery. Despite the great success it enjoyed, selecting the proper primitives from high-dimension primitive set for GP to construct solutions is still a time-consuming and challenging issue that limits the efficacy of GP in real-world applications. In this paper, we propose a multi-population GP framework with adaptively weighted primitives to address the above issues. In the proposed framework, the entire population consists of several sub-populations and each has a different vector of primitive weights to determine the probability of using the corresponding primitives in a sub-population. By adaptively adjusting the weights of the primitives and periodically sharing information between sub-populations, the proposed framework can efficiently identify important primitives to assist the search. Furthermore, based on the proposed framework and the graphics processing unit computing technique, a high-performance self-learning gene expression programming algorithm (HSL-GEP) is developed. The HSL-GEP is tested on fifteen problems, including four real-world problems. The experimental results have demonstrated that the proposed HSL-GEP outperforms several state-of-the-art GPs, in terms of both solution quality and search efficiency. |
Year | DOI | Venue |
---|---|---|
2020 | 10.1007/s00500-019-04379-4 | Soft Computing |
Keywords | DocType | Volume |
Genetic programming, Multi-population, Adaptive-weight, GPU | Journal | 24 |
Issue | ISSN | Citations |
10 | 1432-7643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhixing Huang | 1 | 1 | 0.69 |
Jing-hui Zhong | 2 | 380 | 33.00 |
Liang Feng | 3 | 118 | 14.17 |
Mei Yi | 4 | 941 | 53.85 |
Wentong Cai | 5 | 1928 | 197.81 |