Genetic Algorithm Parameter Tuning
Genetic Algorithm Parameter Tuning is the systematic process of selecting and adjusting the operational parameters of a genetic algorithm—such as population size, crossover and mutation rates, and selection mechanisms—to optimize its performance in terms of convergence speed, solution quality, and robustness across problem instances.
1.3K
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69.8K
Citations
3.4K
Authors
1.2K
Institutions
Publications per year
2017–2026
295
Variants
Ga Parameter Tuning
Parents
1.3K
Salvador García, Daniel Molina, Manuel Lozano et al. · Journal of Heuristics · 2008 · 1.7K citations
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3.4K
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University of Illinois Urbana-Champaign, United States
9 publications · 290 citations
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8
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King Fahd University of Petroleum and Minerals, Saudi Arabia
7 publications · 371 citations
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Universidad de Granada
Granada, Spain
16 h-index
50 publications
9.8K citations
University of Technology Malaysia
Johor Bahru, Malaysia
13 h-index
25 publications
2K citations
University of Illinois Urbana-Champaign
Urbana, United States
12 h-index
29 publications
1.1K citations

Centre National de la Recherche Scientifique
Paris, France
11 h-index
25 publications
1K citations

Sharif University of Technology
Tehran, Iran
10 h-index
12 publications
315 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
9 h-index
27 publications
4.5K citations

National Yang Ming Chiao Tung University
Hsinchu, Taiwan
8 h-index
11 publications
815 citations

King Fahd University of Petroleum and Minerals
Dhahran, Saudi Arabia
8 h-index
14 publications
729 citations
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25 publications · 1.2K citations
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19 publications · 2K citations
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16 publications · 777 citations
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12 publications · 914 citations
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11 publications · 3.1K citations
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