Comparative statistical results obtained against benchmark functions. by Serdar Ekinci (15927455)

Multi-Objective Cooperative Paths Planning for Multiple Parafoils System Using a Genetic Algorithm by Chen Qi (2803873)

“…Parafoils’ paths are encoded by real matrix, and the cooperative relationship between parafoils is realized by paths fitness function. The random single point crossover and Gaussian mutation are introduced to accelerate algorithm convergence rate. …”

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

“…A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …”

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

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

“…<i>Quantum Anamorphosis</i> addresses this challenge through physically motivated localization of molecular orbitals and site reordering, which yield unique block-diagonal Hamiltonian matrices and compact spin-adapted many-body wave functions. In this work, we introduce a genetic algorithm to identify optimal orbital/site orderings that enhance wave function compactness, thereby enabling the study of larger systems than previously possible. …”

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

Subjects:

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

“…</p><p dir="ltr">The package includes:</p><ul><li>Scripts for first-order analysis, third-order modeling, optimization using a Physically Grounded Merit Function (PGMF), and RMS-based refinement.…”

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. …”

Proposed algorithm. by Keenjhar Ayoob (22339616)

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”

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

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

Subjects:

Flowchart of one iteration of the CE-Symm algorithm. by Spencer E. Bliven (6672755)

a) FO Function, b) FI function. by Huang Jiexian (17060975)

“…The SRL32 primitive (Reconfigurable Look up Tables—RLUTs) and DPR (Dynamic Partial Reconfiguration) are employed to reconfigure single round MISTY1 / KASUMI algorithms on the run-time. The RLUT based architecture attains dynamic logic functionality without extra hardware resources by internally modifying the LUT contents. …”

IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

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

Flowchart of improved FOX optimization algorithm. by Mahmood A. Jumaah (22272371)

“…Additionally, it achieved 880 wins, 228 ties, and 348 losses against 16 optimization algorithms across all involved functions and problems. …”

Benchmark functions. by Yun Qi (560401)