Genetic Programming with Diversity Enhancement Strategy for Feature Learning in Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.9244

@InProceedings{Liang:2026:CEC,

• author = "Jing Liang and Zheng Li and Peng Wang2 and Xiangyang Ren and Ying Bi and Caitong Yue",
• title = "Genetic Programming with Diversity Enhancement Strategy for Feature Learning in Classification",
• booktitle = "IEEE CEC 2026",
• year = "2026",
• editor = "Nelishia Pillay and Yew-Soon Ong and Carlos Coello and Juergen Branke and Simon Lucas and Chuan-Kang Ting and Bing Xue",
• pages = "cec pap288",
• address = "Maastricht, the Netherlands",
• month = "21-26 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, feature engineering, high-dimensional classification",
• URL = "https://wcci.klinkhamergroup.com/papers/by_date.html#cec_pap288",
• URL = "https://linklings.s3.amazonaws.com/organizations/WCCI/wcci2026/submissions/stype102/WPPBT-cec_pap288s2.pdf",
• size = "8 pages",
• abstract = "The proliferation of high-dimensional data across various domains presents a substantial challenge for classification tasks. To address this challenge, effective feature engineering methods are critical for building robust predictive models. However, existing methods often struggle to gain comprehensive insight and develop reliable models in complex scenarios. This paper introduces a novel genetic programming method for feature learning in classification, which adopts a diversity enhancement strategy to balance exploration and exploitation. The effectiveness of the proposed method is validated on 11 real-world datasets and compared with representative feature selection and feature construction methods. The experimental results indicate that the proposed method achieves superior classification performance and generates structurally diverse tree architectures, confirming its strong capability to maintain diversity and explore a wider feature representation space.",
• notes = "Zhengzhou University, Henan Institute of Technology, China",

}

Genetic Programming entries for Jing Liang Zheng Li Peng Wang2 Xiangyang Ren Ying Bi Caitong Yue

Citations