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Showing 21 - 40 results of 16,182 for search '(( algorithms used function ) OR ((( algorithm python function ) OR ( algorithm 1 function ))))', query time: 0.53s Refine Results
  1. 21
    Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms.

    Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

    Published 2023
    “…<p>Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms.…”
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  2. 22
    Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>14−<i>f</i>23.

    Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>14−<i>f</i>23. by Ying Li (38224)

    Published 2024
    “…<p>Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>14−<i>f</i>23.…”
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  3. 23
    Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>8−<i>f</i>13.

    Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>8−<i>f</i>13. by Ying Li (38224)

    Published 2024
    “…<p>Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>8−<i>f</i>13.…”
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  4. 24
    The parameters of algorithms.

    The parameters of algorithms. by Le Huu Binh (18340233)

    Published 2025
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  5. 25
    Distribution of DLCO in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians.

    Distribution of DLCO in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians. by Yuh-Chin T. Huang (17867207)

    Published 2024
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  6. 26
    Restoration process via Algorithm 1.

    Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

    Published 2024
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  7. 27
    Restoration process via Algorithm 1.

    Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

    Published 2024
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  8. 28
    Restoration process via Algorithm 1.

    Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

    Published 2024
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  9. 29
    Restoration process via Algorithm 1.

    Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

    Published 2024
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  10. 30
    Restoration process via Algorithm 1.

    Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

    Published 2024
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  11. 31
    S1 File -

    S1 File - by Yuh-Chin T. Huang (17867207)

    Published 2024
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  12. 32
    S1 Dataset -

    S1 Dataset - by Yuh-Chin T. Huang (17867207)

    Published 2024
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  13. 33
    Critical values of membership functions learned from the study cohort by the algorithm.

    Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

    Published 2023
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  14. 34
    Rosenbrock function.

    Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  15. 35
    Rosenbrock function.

    Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  16. 36
    Rastrigin function.

    Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  17. 37
    Rosenbrock function.

    Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  18. 38
    Rastrigin function.

    Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  19. 39
    Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms.

    Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms. by Yao Peng (1928524)

    Published 2023
    “…<p>Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms.…”
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  20. 40
    Eight commonly used benchmark functions.

    Eight commonly used benchmark functions. by Guangwei Liu (181992)

    Published 2023
    “…Secondly, the nonlinear convergence factor is constructed to replace the original random factor <i>c</i><sub>1</sub> to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. …”
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