Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)

Convergence curves of the IMGO and comparison algorithms on functions <i>f</i>1−<i>f</i>7. by Ying Li (38224)

“…<p>Convergence curves of the IMGO and comparison algorithms on functions <i>f</i>1−<i>f</i>7.</p>…”

Parameters <i>p</i> identified by the APLM algorithm. by HaiYan Jiang (4455199)

Performance of algorithms compared to a smaller <i>RNN.</i> by Swathi Anil (17382903)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art DE variants using CEC2014 functions. by Yang Cao (53545)

Data Sheet 1_IGSA-SAC: a novel approach for intrusion detection using improved gravitational search algorithm and soft actor-critic.docx by Lizhong Jin (20991293)

“…The results demonstrate that IGSA-SAC achieves an accuracy of 84.15% and an F1-score of 84.85% on the NSL-KDD dataset. On the AWID dataset, IGSA-SAC surpasses 98.9% in both accuracy and F1-score, outperforming existing intrusion detection algorithms.…”

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

Flowchart of the algorithm. by Xiaowen Zhang (83669)

Subjects:

The parameters of algorithms. by Le Huu Binh (18340233)

Subjects:

Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”

Convergence curve of functions <i>f</i>_<i>1</i><i>-f</i>_<i>10</i> in D = 10. by Yu Li (183836)

Probably pathogenic variants (algorithms). by Felipe Fidalgo (642532)

Compare the test functions. by Yanqiu Li (683621)

Average reward variation of three models. by Yi Fei (5067731)

“…Next, a DRL-based self-efficient lateral motion strategy for AVs is proposed, with state and reward functions tailored to the environmental features of the diverging area. …”

Computational time of each algorithm—Part I. by Xuan Chen (158617)

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