Population-Based Redundancy Control in Genetic Algorithms: Enhancing Max-Cut Optimization
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| Publicado en: | Mathematics vol. 13, no. 9 (2025), p. 1409 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3203208641 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13091409 |2 doi | |
| 035 | |a 3203208641 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Yong-Hyuk, Kim |u School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea; yhdfly@kw.ac.kr | |
| 245 | 1 | |a Population-Based Redundancy Control in Genetic Algorithms: Enhancing Max-Cut Optimization | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The max-cut problem is a well-known topic in combinatorial optimization, with a wide range of practical applications. Given its NP-hard nature, heuristic approaches—such as genetic algorithms, tabu search, and harmony search—have been extensively employed. Recent research has demonstrated that harmony search can outperform genetic algorithms by effectively avoiding redundant searches, a strategy similar to tabu search. In this study, we propose a modified genetic algorithm that integrates tabu search to enhance solution quality. By preventing repeated exploration of previously visited solutions, the proposed method significantly improves the efficiency of traditional genetic algorithms and achieves performance levels comparable to harmony search. The experimental results confirm that the proposed algorithm outperforms standard genetic algorithms on the max-cut problem. This work demonstrates the effectiveness of combining tabu search with genetic algorithms and offers valuable insights into the enhancement of heuristic optimization techniques. The novelty of our approach lies in integrating solution-level tabu constraints directly into the genetic algorithm’s population dynamics, enabling redundancy prevention without additional memory overhead, a strategy not previously explored in the proposed hybrids. | |
| 653 | |a Machine learning | ||
| 653 | |a Semidefinite programming | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Graphs | ||
| 653 | |a Trends | ||
| 653 | |a Combinatorial analysis | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Tabu search | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Heuristic | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Efficiency | ||
| 653 | |a Redundancy | ||
| 700 | 1 | |a Geem Zong Woo |u Department of Smart City, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Republic of Korea; geem@gachon.ac.kr | |
| 700 | 1 | |a Yoon Yourim |u Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Republic of Korea | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 9 (2025), p. 1409 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203208641/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3203208641/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203208641/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |