Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

<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.…”

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined. by Yuanchen Zhao (12905580)

“…The reaction network is assumed to form a linear degradation chain 1 → 2 → ⋯ → <i>N</i> with the end-product concentration (metabolite <i>N</i>, orange) taken as the function of interest (shown with <i>N</i> = 3 as an example). …”

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

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

Automatic cropping algorithm. by Thomas W Georgi (12427189)

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Experiment 2D and 5D: Progressive Sample Scaling Algorithm To Solve Many-Affine BBOB Functions. by Boris Almonacid (4110337)

“…For this specific experiment<a href="https://markdownlivepreview.com/#cite_note-2" target="_blank">^[2]</a><a href="https://markdownlivepreview.com/#cite_note-3" target="_blank">^[3]</a>, the average AOCC is calculated.</p><p dir="ltr"><b>Objectives</b></p><ul><li>Solve Many-Affine BBOB Functions using a Deterministic Algorithm.…”

Similarity between the hierarchical community structure of structural and functional brain graphs. by Arian Ashourvan (6685232)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Both versions are evaluated on the same synthetic datasets with a 3-group ground truth. Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

Comparison of compression rates of DDQR and Geco3 using simulated data around the region where the performance of DDQR exceeds that of GeCo3. by Yujun Wang (1947196)

As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algor... by Daniela Huppenkothen (9174507)

“…We note that the middle row is a special case: here, <i>f</i>_target only corresponds to the proportions of the embedded cohort, while “success” for these two panels is defined as recovering maximally diverse cohorts, as this particular algorithm is designed to do. The bottom row shows the same simulations as the middle row, but presents successes and failures when the targets that generated the embedded cohort are applied instead of the balanced targets that are part of the baseline’s objective function. …”

Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <pre><code># the main function</code></pre> <pre><code># 1- Instrument the input source, testfunc.…”

The GOR4 and SOPMA algorithm findings. by Ahmad Abdullah Mahdeen (19704401)

Comparison of optimization performance of six ABC algorithms. by Shuhui Yu (1535500)

Brief sketch of the quasi-attraction/alignment algorithm. by Takayuki Niizato (162226)

“…(D) A brief sketch of the avoidance algorithm. Upper: Each direction is extended to the repulsion area = {<b><i>r</i></b>||<b><i>r</i></b>| = <i>R</i>}, where is the minimal sphere cap that covers all points on . …”

An example of pairwise distance tables between three sites where each site has three nodes: Site₁={<i>A</i>, <i>B</i>, <i>C</i>}, Site₂={<i>a</i>, <i>b</i>, <... by Shuwen Wang (457921)