Details

Autor(en) / Beteiligte

• Huang, Zhixing
• Zhong, Jinghui
• Feng, Liang
• Mei, Yi
• Cai, Wentong

Titel

A fast parallel genetic programming framework with adaptively weighted primitives for symbolic regression

Ist Teil von

• Soft computing (Berlin, Germany), 2020-05, Vol.24 (10), p.7523-7539

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2020

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• 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.