The PF of ten comparative algorithms on the MMF1 test function. by Wenxing Wu (16853002)

“…<p>The PF of ten comparative algorithms on the MMF1 test function.</p>…”

The PS of ten comparative algorithms on the MMF1 test function. by Wenxing Wu (16853002)

“…<p>The PS of ten comparative algorithms on the MMF1 test function.</p>…”

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

Comparison of algorithm performance in ZDT1 and ZDT2 function tests. by Tao Dong (15551)

“…<p>Comparison of algorithm performance in ZDT1 and ZDT2 function tests.…”

Performance comparison of algorithms in ZDT5 and ZDT6 function tests. by Tao Dong (15551)

Comparison of algorithm performance in ZDT3 and ZDT4 function tests. by Tao Dong (15551)

Autocorrelation functions <i>C</i>(<i>r</i>, <i>t</i>) as functions of the rescaled distance <i>r</i>/<i>R</i>(<i>t</i>) for several time points, where <i>R</i>(<i>t</i>) is determ... by Marc Durand (556746)

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…For a RDKit molecule, it provides (i) a PNG binary string with an image of the molecule with color-highlighted functional groups; (ii) a list of sets of atom indices (idx), each set corresponding to a functional group; (iii) a list of pseudo-SMILES canonicalized strings for the full functional groups; and (iv) a list of RDKit labeled mol objects, one for each full functional group. …”

Comparison of the smooth-index (‘SI’) algorithm with feedback arc set (‘FAS’) sorting. by Alexander Borst (32619)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

<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.…”

A) Ranking of models trained on different feature sets across all tested algorithms, sorted by their F1-score. by Jose Cleydson F. Silva (8685594)

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

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

Subjects:

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Sorting time is nominally similar between automated and manual sorting, but automation enables walk-away functionality. by Diane M. Wiener (1956583)

Subjects:

Parameters of trained algorithms. by Swathi Anil (17382903)

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

“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”

Python scripts used to model CPU errors by Poisson distribution density mass function and to draw graph of Poisson events (needs Matplot, SciPy, PysimpleGUI and NumPy) by Santhosh Kumar Rajamani (13023678)

Algorithm parameter settings. by Tao Dong (15551)

“…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”