Convergence curve of others functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Convergence curve of multimodal functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Convergence curve of multimodal functions compared with the PSO family algorithms. by Yong Gao (29929)

The execution times (nano seconds) per particle are measured for four different algorithms [47], (A: first algorithm, B: second algorithm, C: third algorithm: and D: fourth algorit... by Abedalmuhdi Almomany (18547669)

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)

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)

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

Comparison of overall (a) Accuracy and (b) Label request results between ROAL and AOL. by Honglan Huang (341951)

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)

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 use of the slightly off-axis strategies. by Raul Castañeda (13932971)

Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. by Raul Castañeda (13932971)

Comparative performance evaluation of 3Mont with other feature selection algorithms over 10 iterations. by Miray Unlu Yazici (14792680)

Subjects:

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)

Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. by Panfei Li (22379086)

“…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…”

The SSIM for the different algorithms. by Bingbing Li (461702)

“…Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. …”

The number of core promoter elements and TF binding sites predicted for four promoters with YAPP, GPMiner and ElemeNT algorithms. by Ali Hosseini Rad S. M. (9167497)

Skeletonisation algorithm. by Jordan Hembrow (16908051)