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Showing 1 - 20 results of 91 for search '(( binary mapk driven optimization algorithm ) OR ( less based small optimization algorithm ))*', query time: 0.64s Refine Results
  1. 1
    Optimal ALARM BN discovered by CausNet.

    Optimal ALARM BN discovered by CausNet. by Nand Sharma (21519845)

    Published 2025
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  2. 2
    APL, ACC and ACC /APL of the optimal benchmark networks with different mixing parameter.

    APL, ACC and ACC /APL of the optimal benchmark networks with different mixing parameter. by Ruochen Zhang (3434996)

    Published 2024
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  3. 3
    The Pareto-optimal fronts on the random network with 10 added edges and its corresponding solutions.

    The Pareto-optimal fronts on the random network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

    Published 2024
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  4. 4
    The Pareto-optimal fronts on the regular network with 10 added edges and its corresponding solutions.

    The Pareto-optimal fronts on the regular network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

    Published 2024
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  5. 5
    APL, ACC and ACC /APL of the optimal networks with different goals and number of added edges.

    APL, ACC and ACC /APL of the optimal networks with different goals and number of added edges. by Ruochen Zhang (3434996)

    Published 2024
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  6. 6
    Optimal ovarian cancer BNs learned by CausNet and CausNet-partial.

    Optimal ovarian cancer BNs learned by CausNet and CausNet-partial. by Nand Sharma (21519845)

    Published 2025
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  7. 7
    Percentage of edges added within the same community in the optimal benchmark networks with different mixing parameter and goals.

    Percentage of edges added within the same community in the optimal benchmark networks with different mixing parameter and goals. by Ruochen Zhang (3434996)

    Published 2024
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  8. 8
    The flow of the SP-DRL algorithm.

    The flow of the SP-DRL algorithm. by Jie Fang (306330)

    Published 2023
    “…The classical packing solution method relies on heuristic and metaheuristic algorithms. Usually, it needs to be designed with manual decisions to guide the solution, resulting in a small solution scale, weak generalization, and low solution efficiency. …”
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  9. 9
    The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization

    The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization by James A. Lumley (9464878)

    Published 2020
    “…We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. …”
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  10. 10
    The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization

    The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization by James A. Lumley (9464878)

    Published 2020
    “…We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. …”
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  11. 11
    Illustration of crossover.

    Illustration of crossover. by Ruochen Zhang (3434996)

    Published 2024
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  12. 12
    The mean value of the maximum ACC for each number of added edges <i>k</i> for 10 runs acquired by MOEA/D-SW, MA-SW, GA-SW, GR-SW, NW model, and NSGA-II.

    The mean value of the maximum ACC for each number of added edges <i>k</i> for 10 runs acquired by MOEA/D-SW, MA-SW, GA-SW, GR-SW, NW model, and NSGA-II. by Ruochen Zhang (3434996)

    Published 2024
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  13. 13
    S1 Dataset -

    S1 Dataset - by Ruochen Zhang (3434996)

    Published 2024
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  14. 14
    The topologies of edging solution with <i>k</i> = 10 on Zachary’s Karate Club network.

    The topologies of edging solution with <i>k</i> = 10 on Zachary’s Karate Club network. by Ruochen Zhang (3434996)

    Published 2024
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  15. 15
    The mean value of the maximum ACC/APL for each number of added edges <i>k</i> for 10 runs acquired by MOEA/D-SW, MA-SW, GA-SW, GR-SW, NW model, and NSGA-II.

    The mean value of the maximum ACC/APL for each number of added edges <i>k</i> for 10 runs acquired by MOEA/D-SW, MA-SW, GA-SW, GR-SW, NW model, and NSGA-II. by Ruochen Zhang (3434996)

    Published 2024
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  16. 16
    Statistical tests of ACC on the random network.

    Statistical tests of ACC on the random network. by Ruochen Zhang (3434996)

    Published 2024
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  17. 17
    Parameters in the experiment.

    Parameters in the experiment. by Ruochen Zhang (3434996)

    Published 2024
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  18. 18
    The mean value of the maximum ACC/APL for each number of added edges <i>k</i> and different crossover probability <i>P</i><sub><i>c</i></sub> for 10 runs acquired by MOEA/D-SW on the regular network.

    The mean value of the maximum ACC/APL for each number of added edges <i>k</i> and different crossover probability <i>P</i><sub><i>c</i></sub> for 10 runs acquired by MOEA/D-SW on t... by Ruochen Zhang (3434996)

    Published 2024
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  19. 19
    Statistical tests of APL on the random network.

    Statistical tests of APL on the random network. by Ruochen Zhang (3434996)

    Published 2024
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  20. 20
    The mean value of the minimum APL for each number of added edges <i>k</i> for 10 runs acquired by MOEA/D-SW, MA-SW, GA-SW, GR-SW, NW model, and NSGA-II.

    The mean value of the minimum APL for each number of added edges <i>k</i> for 10 runs acquired by MOEA/D-SW, MA-SW, GA-SW, GR-SW, NW model, and NSGA-II. by Ruochen Zhang (3434996)

    Published 2024
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