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algorithm python » algorithm within (Expand Search), algorithms within (Expand Search), algorithm both (Expand Search)
algorithm reward » algorithm lennard (Expand Search), algorithm towards (Expand Search), algorithm reduced (Expand Search)
python function » protein function (Expand Search)
reward function » related functions (Expand Search)
algorithm i » algorithm _ (Expand Search), algorithm b (Expand Search), algorithm a (Expand Search)
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Comparative statistical performance of various algorithms in minimizing the <i>F</i> cost function.
Published 2023“…<p>Comparative statistical performance of various algorithms in minimizing the <i>F</i> cost function.…”
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CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages
Published 2024“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”
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CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages
Published 2024“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”
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CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages
Published 2024“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”
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Comparison of algorithm performance in ZDT1 and ZDT2 function tests.
Published 2025“…<p>Comparison of algorithm performance in ZDT1 and ZDT2 function tests.…”
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<i>K</i>-CDFs: A Nonparametric Clustering Algorithm via Cumulative Distribution Function
Published 2022“…<p>We propose a novel partitioning clustering procedure based on the cumulative distribution function (CDF), called <i>K</i>-CDFs. For univariate data, the <i>K</i>-CDFs represent the cluster centers by empirical CDFs and assign each observation to the closest center measured by the Cram<math><mrow><mi>e</mi><mo>´</mo></mrow></math>r-von Mises distance. …”