MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection
Abstract 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 limitat...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00112-4 |
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| _version_ | 1849225879150919680 |
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| author | Chuantao Li Chen Huang Ruihan Chen Zhuohong Yu Sheng Li |
| author_facet | Chuantao Li Chen Huang Ruihan Chen Zhuohong Yu Sheng Li |
| author_sort | Chuantao Li |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-ecc918bf629441b0b1c80c679a2a4701 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-ecc918bf629441b0b1c80c679a2a47012025-08-24T11:53:57ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137712910.1007/s44443-025-00112-4MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selectionChuantao Li0Chen Huang1Ruihan Chen2Zhuohong Yu3Sheng Li4School of Mathematics and Computer, Guangdong Ocean UniversitySchool of Mathematics and Computer, Guangdong Ocean UniversitySchool of Mathematics and Computer, Guangdong Ocean UniversitySchool of Mathematics and Computer, Guangdong Ocean UniversitySchool of Mathematics and Computer, Guangdong Ocean UniversityAbstract 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.https://doi.org/10.1007/s44443-025-00112-4Genetic algorithmMulti-populationDynamic competitionCoevolutionaryFeature selection |
| spellingShingle | Chuantao Li Chen Huang Ruihan Chen Zhuohong Yu Sheng Li MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection Journal of King Saud University: Computer and Information Sciences Genetic algorithm Multi-population Dynamic competition Coevolutionary Feature selection |
| title | MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection |
| title_full | MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection |
| title_fullStr | MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection |
| title_full_unstemmed | MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection |
| title_short | MPDCGA: a real-coded multi-population dynamic competitive genetic algorithm for feature selection |
| title_sort | mpdcga a real coded multi population dynamic competitive genetic algorithm for feature selection |
| topic | Genetic algorithm Multi-population Dynamic competition Coevolutionary Feature selection |
| url | https://doi.org/10.1007/s44443-025-00112-4 |
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