Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…The availability of quantitative target profiles, from genetic perturbations or from clinical phenotypes, provides additional information that can be leveraged to improve the identification of cancer related gene sets by discovering groups with complementary functional associations with such targets. …”

Algorithm operation steps. by Junyan Wang (4738518)

“…To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. …”

Table_1_Clinical Functional Seizure Score (CFSS): a simple algorithm for clinicians to suspect functional seizures.DOCX by Mohammad Dashtkoohi (15417886)

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)

Flowchart for Algorithm 2. by Sagvan Y. Musa (17797611)

Tuned parameters of algorithms. by Kaveh Faraji Googerdchi (18778727)

IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

Visual depiction of the PCIA algorithm. by Kayson Fakhar (12890364)

General idea of alternative structures of aptamers binding the same ligand and its specific implementation scheme for the BioRS function. by Alexander V. Spirov (11302085)

CEC2017 basic functions. by Tengfei Ma (597633)

“…The optimal individual’s position is updated by randomly selecting from these factors, enhancing the algorithm’s ability to attain the global optimum and increasing its overall robustness. …”

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Pseudo-code of MWOA algorithm. by Yi-Qiang Xia (20161326)

“…<div><p>This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. …”

Parameter settings for metaheuristic algorithms. by Junhao Wei (6816803)

“…<div><p>Whale Optimization Algorithm (WOA) suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. …”

Effective cluster radius from Ripley’s H-function for the NPC dataset. by Daniel F. Nino (9745709)

CEC2017 test function test results. by Tengfei Ma (597633)

“…The optimal individual’s position is updated by randomly selecting from these factors, enhancing the algorithm’s ability to attain the global optimum and increasing its overall robustness. …”

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. …”

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

“…<div><p>This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. …”