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)

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Schematic diagram of dynamic step size expansion. by Zhenggang Wang (1753657)

Subjects: “…target gravitational function…”

Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

“…To choose the next pipeline configuration to evaluate, the BO algorithm uses an Expected Improvement function to trade off maximisation of QS with the need to fully learn the GP. …”

Analysis of ABIDE dataset: Conventional regression frameworks for optimized algorithmic value. by Alexandria M. Jensen (6494957)

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Fitness function parameters. by Ankita Sharma (3183054)

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

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Algorithm parameters. by Ruyi Dong (9038174)

Subjects: “…dimensional benchmark functions…”

Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

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Penalty functions used in different methods. by Souvik Seal (12437166)

Analysis of the limit cases of the sampling function. by Greta Grassmann (12418506)

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The control algorithm of MPC for SSI. by Ahmed Abdelaleem (19066115)

Swarm intelligence algorithms for multi-objective IMP: Step-by-step improvement by Francisco Muñoz (9455441)

“…Algorithms implemented on article "<b>A step-by-step solution for the multi-objective influence spread problem</b>". …”

ANOVA was performed to test the differences between the algorithms for each indicator. by Jihui Hu (19812736)

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Comparison of proposed model with other methods using same datasets. by Ahmed Bahaa (12887063)

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

“…The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. …”

Experimental optimization results for dimension 10 using reported competitor algorithms on the unimodal, multimodal, hybrid, and composite of CEC2022 benchmark functions. by WanRu Zhao (18980374)

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Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

Distribution of cross correlations in functional connectivity in ABIDE sample. by Alexandria M. Jensen (6494957)

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Summary of classical and CEC-based benchmark test functions used in this study. by Mahmood A. Jumaah (22272371)

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Node matching step 1. by Shuwen Wang (457921)

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