The number of inactive nodes as a function of time steps at higher values of <i>P</i> and <i>α</i> in (a) a random network and (b) a scale-free network. by Waseem Al-Aqqad (14123917)

BEs with different loss functions. by Qin Gong (118801)

“…Next, we select the gamma distribution as the prior distribution and apply the Lindley approximation algorithm to calculate `estimates of Shannon entropy and Rényi entropy under different loss functions including Linex loss function, entropy loss function, and DeGroot loss function respectively. …”

Constructing the dynamical law. by Robert Marsland (8616483)

Algorithm parameter levels. by Peng Yao (448155)

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

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

The parameter settings for real data. by Yanju Zhang (437336)

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Cost results on the real datasets. by Yanju Zhang (437336)

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Time results on the real datasets. by Yanju Zhang (437336)

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Highly interrelated genes and their biological functions among 21 cancers. by Qunlun Shen (11668968)

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

“…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. …”

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

“…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. …”

2D Rastrigin function. by Shikun Chen (14625352)

“…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. …”

2D Levy function. by Shikun Chen (14625352)

“…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. …”

2D Rosenbrock function. by Shikun Chen (14625352)

“…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. …”

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

“…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. …”

Functional structure of the system. by Cun Lin (18880471)

Convergence curves on the functions C19F2, C19F3, C19F5, C19F6, C19F10, C21F2, C21F3, C21F9, and C21F10. by Mahmood A. Jumaah (22272371)

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Convergence curves on the functions C17F5, C17F7, C17F8, C17F9, C17F11, C17F19, C17F20, C17F22, and C17F27. by Mahmood A. Jumaah (22272371)

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Friedman test average rankings of IFOX and competing optimization algorithms across CL, C17, C19, C21, C22, and RWPs. The “Position” column indicates the overall rank based on the... by Mahmood A. Jumaah (22272371)

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The parallel coordinates of the non-dominated front obtained by each algorithm (used in the comparison) on DTLZ3 and DTLZ7 with 3 objectives. by Heba Askr (15572851)

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