Convergence curves of the mean function error values for six test functions at <i>D</i> = 30. by Xuxu Zhong (10099054)

Population diversity comparison on all function (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP. by Jin Ding (11522)

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

“…(B) A sketch of the cover function, which returns the minimum cap on the interaction sphere , which covers all points <b><i>p</i></b>_<i>i</i> (for the mathematical definition, see the Section 2 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010869#pcbi.1010869.s008" target="_blank">S1 Appendix</a>). …”

Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

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

RMSE comparison in all test function (a) F1 (b) F2 (c) F3 (d) F4 (e) F5 (f) F6 (g) F7 (h) F8 (i) F9 (j) F10. by Jin Ding (11522)

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

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. …”

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density. by Hassan Mehboob (8960273)

“…<p>A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.…”

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Fitting results of the different optimization algorithms applied. by Sonja Langthaler (6069149)

Demonstration of the combinatorial Hill functions (A and B), and the regulation function (C and D) under the regulation of an activator and a repressor. by Ruihao Li (2908991)

Example performance of the smooth-index algorithm. by Alexander Borst (32619)

“…<b>D</b> Different cost functions as the algorithm performs the gradient descent. …”

Functional enrichment and correlation analysis of hub DE-SRGs. by Xihao Shen (20347942)

Subjects:

MOEA/D-ND algorithm flowchart. by Jun Sun (48981)

“…The experimental outcomes denote that on the logic function with Zener diode transistor, the proposed algorithm has a mean generation distance index of 5.03E-3, which is lower than most algorithms. …”

Adding a new function module. by Yukako Yamane (9034133)