An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…<i>(c)</i> Pseudocode for self-consistently computing <b>R</b>* and , which is identical to standard expectation-maximization algorithms employed for problems with latent variables in machine learning.…”

Algorithm comparison experiment on the NWPU VHR-10 dataset. by Pingping Yan (462509)

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

“…</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.…”

Combining Deep Learning Neural Networks with Genetic Algorithms to Map Nanocluster Configuration Spaces with Quantum Accuracy at Low Computational Cost by Johnathan von der Heyde (16828390)

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The value functions for different set-up costs. by Liying Wang (288626)

“…These expressions are determined using the copula function, and an algorithm is designed to construct the corresponding transition probability matrix. …”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Pictorial representation of isomorphically scaling down spots within a fixed field of view and corresponding imaging configuration. by Nicholas H. Vitale (20469289)

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Algorithm parameter levels. by Peng Yao (448155)

“…As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. …”

Auto-tuning algorithm of the weight factors in the cost function. by Syed Umaid Ali (14486372)

“…<p>Auto-tuning algorithm of the weight factors in the cost function.…”

Iteration curves of each algorithm. by Peng Yao (448155)

“…As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. …”

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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The probability density function for cost function g(X). by Matheus Sant’Ana Lima (9937809)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

The obtained cost function values with respect to each run of different algorithms. by Davut Izci (15927452)

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Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

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Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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Performance comparison of recent and effective algorithms for minimizing error-based cost functions. by Davut Izci (15927452)

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