文献类型：期刊文献

英文题名：MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection

作者：Li, Chuantao[1];Huang, Chen[1];Chen, Ruihan[1,2];Yu, Zhuohong[1];Li, Sheng[1]

机构：[1]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang 524088, Peoples R China;[2]Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400030, Peoples R China

年份：2025

卷号：37

期号：7

外文期刊名：JOURNAL OF KING SAUD UNIVERSITY COMPUTER AND INFORMATION SCIENCES

收录：SCI-EXPANDED(收录号：WOS:001556773700008)、、Scopus(收录号：2-s2.0-105013863560)、WOS

基金：This study was supported by special grants from the National College Students Innovation and Entrepreneurship Training Program under Grant No. 202410566025, Students Innovation and Entrepreneurship Training Program of Guangdong Ocean University under Grant No. CXXL2024148, Guangdong Provincial Science and Technology Innovation Strategy under Grant No. pdjh2023b0247, and Guangdong Ocean University Undergraduate Innovation Team Project under Grant No. CXTD2023014.

语种：英文

外文关键词：Genetic algorithm; Multi-population; Dynamic competition; Coevolutionary; Feature selection

外文摘要：Feature selection constitutes a fundamental component of machine learning and Genetic Algorithms (GAs) are extensively employed in feature selection. However, conventional GAs are afflicted by premature convergence and difficulty in preserving population diversity. To mitigate these limitations, this study proposes a real-coded multi-population dynamic competitive genetic algorithm (MPDCGA) for feature selection. In this innovative framework, the population initialization mechanism based on mRMR and cosine similarity furnishes a diverse initial solution, the dynamic competition operator explores the optimal feature subset through coevolutionary processes, and the adaptive similarity crossover operator improves the global search efficiency while augmenting the capability to extract potentially salient features. To comprehensively evaluate the performance of MPDCGA, adequate experiments were conducted on 16 UCI datasets. The experimental results demonstrate that MPDCGA effectively circumvents the limitations of local optimality, achieving superior feature selection accuracy and robustness.