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)

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)

Face matching performance as a function of survey condition for Study 2 (threshold = 0.5). by Daniela Barragan (13146762)

Face matching performance as a function of test condition for Study 1 (threshold = 0.5). by Daniela Barragan (13146762)

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)

Performance comparison in CP-EE model (a) SVR model (b) BPNN model (c) RF model (d) MS-GEP model. by Jin Ding (11522)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Comparison of different algorithms. by Jiandong Qiu (20389944)

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). …”

The SCOPer algorithm can be run efficiently on multiple cores. by Nima Nouri (2183760)

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. …”

Convergence graphs of the ten algorithms for the five functions from the CEC 2014 suite. by Juan Yao (293170)

Convergence curve of shift function. by Yong Gao (29929)

CPU usage of benchmark function: (a) CPU Usage during the Execution of Benchmark Functions F21-F30, (b) CPU Usage during the Execution of Benchmark Functions F31-F40 and (c) CPU Us... by Ruyi Dong (9038174)

Iteration curve of benchmark function: (a) Convergence curves of the average best fitness for functions F21-F30, (b) Convergence curves of the average best fitness for functions F3... by Ruyi Dong (9038174)

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

Linear relationship between ln(methadone concentration) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

Linear relationship between ln(methadone metabolism) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

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)