Showing 61 - 80 results of 10,052 for search '(( algorithm from function ) OR ((( algorithm python function ) OR ( algorithms real function ))))', query time: 0.50s Refine Results
  1. 61

    Levy function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  2. 62

    Rastrigin function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  3. 63

    Levy function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  4. 64

    Rastrigin function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  5. 65

    Levy function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  6. 66

    Levy function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  7. 67

    Rastrigin function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  8. 68

    Rastrigin function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  9. 69

    Rosenbrock function losses for . by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  10. 70

    <b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

    Published 2025
    “…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”
  11. 71
  12. 72

    Algorithm parameter levels. by Peng Yao (448155)

    Published 2024
    “…Alternatively, the formulated objective functions often deviate from real-world scenarios. …”
  13. 73

    Optimization outcome for the Rosenbrock function. by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  14. 74

    Optimization outcome for the Rastrigin function. by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  15. 75

    2D Rastrigin function. by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  16. 76

    2D Levy function. by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  17. 77

    2D Rosenbrock function. by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  18. 78

    Optimization outcome for the Levy function. by Shikun Chen (14625352)

    Published 2025
    “…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”
  19. 79
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