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. …”

Running time comparison of different algorithms. by Shuang Guo (294074)

“…<div><p>To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. …”

Samples from IPPs with a mixture of squared exponential intensity function (<i>p</i> = 2). by M. Hassan Tanveer (9596437)

Pictorial representation of the edge-cutting algorithm. by Pratik Mullick (12851011)

RRMSE and SSIM values of reconstructed images by the proposed algorithms and the compared methods for the third study case. by Mona Selim (12969227)

PFC value of algorithms on two datasets. by Xing Chen (140292)

“…<div><p>In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. …”

Sensitivity (<i>d</i>′) and criterion values as a function of prior identity decision (threshold = 0.5). by John J. Howard (9267903)

IEEE CEC 2022 benchmark function specifications. by Yang Gao (18005)

IEEE CEC 2017 benchmark function specifications. by Yang Gao (18005)

Experiment 2D and 5D: Progressive Sample Scaling Algorithm To Solve Many-Affine BBOB Functions. by Boris Almonacid (4110337)

“…</p><p dir="ltr">[2] GECCO 2024 Competition: Anytime Algorithms for Many-affine BBOB Functions <a href="https://markdownlivepreview.com/#cite_ref-2" target="_blank">https://gecco-2024.sigevo.org/Competitions#id_Anytime Algorithms for Many-affine BBOB Functions</a>.…”

The typicality function represents the willingness of passengers to walk to a nearby public transportation stop. by Miklós Mezei (13919384)

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

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

Subjects:

MSVM-RFE algorithm for screening key genes. by Ya xi Zhu (11624730)

High-dimensional benchmark test functions. by Ruyi Dong (9038174)

“…In addition, to verify the performance and robustness of LLSKSO, comparison experiments between LLSKSO and 10 well-known algorithms are conducted on 50 benchmark test functions. …”

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

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

Statistical results of various algorithms. by ZeSheng Lin (20501356)

“…EAWOA demonstrates superior optimization accuracy compared to WOA across 21 test functions, with a notable edge on certain functions. …”

Description of unimodal benchmark functions. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

Description of multimodal benchmark functions. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”