Schematic diagram of the experimental algorithm. by Ronghua Meng (20978441)

Subjects:

Genetic algorithm flowchart. by Junjie He (700642)

Subjects:

Complementary cumulative distribution functions of in degree distribution and fitted power law, lognormal and truncated power law distributions. by Olga Valba (10531121)

Algorithm parameters. by Ruyi Dong (9038174)

“…Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. …”

Tour construction for a weighted graph with a probability function p ∈ [0,1]. by Matheus Sant’Ana Lima (9937809)

Heat maps of mean scores over 50 datasets of the algorithms as a function of relative noise and number of samples. by Yuanchen Zhao (12905580)

Subjects:

The algorithm for achieving the research objectives illustrating the process of developing a mathematical model of the braking moment as a function of tire deformation. by Bartosz Wieczorek (8098352)

“…<p>The algorithm for achieving the research objectives illustrating the process of developing a mathematical model of the braking moment as a function of tire deformation.…”

Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

Subjects:

Benchmark functions. by Yun Qi (560401)

The signal detection algorithm for constructing a neurometric function (the probability of segregation as a function of time) generates acceptable buildup fits at <i>DF</i> = 1, 3, 6, 9. by Quynh-Anh Nguyen (847240)

“…Parameters <i>N</i>_<i>in</i> and <i>C</i>_<i>th</i> are chosen to yield SEM similar to those observed in the spike count data [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>, (Fig.3A in Ref)] and to yield the least-squares error of the experimental buildups (dashed, extracted from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>, (Fig.4 in Ref)] and the computer-simulated neurometric functions (solid) for <i>DF</i> = 1, 3, 6, 9. …”

Membership function of each target. by Tianrui Zhang (2294542)

Subjects:

Parameters of trained algorithms. by Swathi Anil (17382903)

ROC curve for each ML algorithm. by Caroline L. Alves (14271413)

F3 function and convergence curve graph. by Jiangli Yu (4601986)

Information of nodes and pipes of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The topology of the NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The result of Wilcoxon signed-rand test. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

The Simulation and optimization process of pipe diameter selection. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of NYN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Optional pipe diameter and unit price of HN. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”