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selection optimization » reaction optimization (Expand Search), generation optimization (Expand Search), function optimization (Expand Search)
well optimization » wolf optimization (Expand Search), whale optimization (Expand Search), field optimization (Expand Search)
based selection » based detection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
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Showing 1 - 20 results of 465 for search '(( genes based selection optimization algorithm ) OR ( binary based well optimization algorithm ))', query time: 0.77s Refine Results
  1. 1
    DE algorithm flow.

    DE algorithm flow. by Ling Zhao (111365)

    Published 2025
    “…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”
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  2. 2
    Test results of different algorithms.

    Test results of different algorithms. by Ling Zhao (111365)

    Published 2025
    “…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”
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  3. 3
    Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection.

    Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

    Published 2022
    “…<p>We predicted patient states using gene-expression and clinical predictors with feature selection (Analysis 5). …”
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  4. 4
    Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE46691.

    Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE46691. by Stephen R. Piccolo (8367780)

    Published 2022
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  5. 5
    Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE10320.

    Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE10320. by Stephen R. Piccolo (8367780)

    Published 2022
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  6. 6
    Top 50 genes according to average rank across feature-selection algorithms for GSE10320 and GSE46691.

    Top 50 genes according to average rank across feature-selection algorithms for GSE10320 and GSE46691. by Stephen R. Piccolo (8367780)

    Published 2022
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  7. 7
    Illustration of the BRIDES algorithm and the selection process.

    Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

    Published 2022
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  8. 8
    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX

    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

    Published 2022
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  9. 9
    Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection.

    Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

    Published 2022
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  10. 10
    Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection with hyperparameter optimization.

    Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection with hyperparameter optimization. by Stephen R. Piccolo (8367780)

    Published 2022
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  11. 11
    Key gene identification based on two machine learning algorithms.

    Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

    Published 2025
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  12. 12
    UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms.

    UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)

    Published 2025
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  13. 13
    Data_Sheet_3_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_3_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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  14. 14
    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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  15. 15
    Data_Sheet_2_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.ZIP

    Data_Sheet_2_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.ZIP by Vishnu Ramasubramanian (4951354)

    Published 2021
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  16. 16
    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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  17. 17
    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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  18. 18
    Data_Sheet_1_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.PDF

    Data_Sheet_1_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.PDF by Vishnu Ramasubramanian (4951354)

    Published 2021
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  19. 19
    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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  20. 20
    DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF

    DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF by Deniz Seçilmiş (13028616)

    Published 2022
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