Algorithm of genetic workup. by Idoia Martinez de Lapiscina (16528261)

Algorithmic image feature identification. by Alex T. Keeley (16612727)

Results of searching performance of the improved NAG algorithm on Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms. by Ruyi Dong (9038174)

“…<p>Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms.…”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. …”

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)

<b>The Application of the Optimized Genetic Algorithm in SerDes Circuit Design</b> by Xuan Wei (18564826)

“…<p dir="ltr">This paper aims to solve the complex design problem of replacing inefficient, traditional parallel structures with high-speed serial interface SerDes. The Genetic Algorithm (GA) is optimized by adding a selection and substitution procedure. …”

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

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. …”

Schematics for gmxapi operations. by M. Eric Irrgang (4487278)

gmxapi usage examples. by M. Eric Irrgang (4487278)

The denoising results yielded by different algorithms. by Limin Ma (556873)

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. …”

Evolutionary processes on special functions (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP. by Jin Ding (11522)

Limitation of the conventional DBT reconstruction algorithm. by Yunsu Choi (11993484)