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structure optimization » structural optimization (Expand Search), structure determination (Expand Search)
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Showing 1 - 20 results of 34 for search '(( gene 31 structure optimization algorithm ) OR ( binary based small optimization algorithm ))', query time: 0.81s Refine Results
  1. 1
    Small-scale dataset comparative analysis using computation resources.

    Small-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

    Published 2023
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  2. 2
    Small-scale dataset comparative analysis using the number of features selected.

    Small-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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  3. 3
    Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100.

    Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  4. 4
    Small-scale dataset comparative analysis using fitness and cost values for population sizes 50 and 100.

    Small-scale dataset comparative analysis using fitness and cost values for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  5. 5
    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

    Published 2023
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  6. 6
    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50... by Olaide N. Oyelade (14047002)

    Published 2023
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  7. 7
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

    Published 2023
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  8. 8
    General structure and operation of the GEP model [66].

    General structure and operation of the GEP model [66]. by Saeid Jamshidi (1481203)

    Published 2020
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  9. 9
    Data_Sheet_7_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_7_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  10. 10
    Data_Sheet_4_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx

    Data_Sheet_4_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  11. 11
    Data_Sheet_6_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_6_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  12. 12
    Data_Sheet_5_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx

    Data_Sheet_5_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  13. 13
    Convergence curves for the applied algorithms.

    Convergence curves for the applied algorithms. by Eslam Hamouda (5172083)

    Published 2024
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  14. 14
    <i>hi</i>PRS algorithm process flow.

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
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  15. 15
    Metaheuristic algorithms for solving DNA-related problems.

    Metaheuristic algorithms for solving DNA-related problems. by Eslam Hamouda (5172083)

    Published 2024
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  16. 16
    Datasets and their properties.

    Datasets and their properties. by Olaide N. Oyelade (14047002)

    Published 2023
    “…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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  17. 17
    Parameter settings.

    Parameter settings. by Olaide N. Oyelade (14047002)

    Published 2023
    “…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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  18. 18
    DataSheet1_Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus.PDF

    DataSheet1_Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus.PDF by Wen Ma (446751)

    Published 2022
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  19. 19
    Optimal Timings Codebook.xlsx

    Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

    Published 2021
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  20. 20
    Optimal Time Intervals of Breech Births Dataset

    Optimal Time Intervals of Breech Births Dataset by Emma Spillane (11249121)

    Published 2021
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