The test error of RF and MS-GEP. by Jin Ding (11522)

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The K-expression of CP-EE model. by Jin Ding (11522)

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Performance metrics of comparative experiment. by Jin Ding (11522)

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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)

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Evaluation metrics graphs for different loss functions. by Huiying Zhang (200681)

Distribution of small airway disease index (SADI in degree angle) in pulmonary function tests with FEV1/FVC ≥ 0.7. by Yuh-Chin T. Huang (17867207)

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Cleavage time as a function of scaled fiber diameter. by Roukayatou R. Ouedraogo (20460980)

The details of the Scelestial algorithm. by Mohammad-Hadi Foroughmand-Araabi (6658772)

“…<p>The inputs to the Scelestial algorithm are a) a set of sequences <i>S</i>, b) the degree of restriction of the restricted Steiner tree <i>k</i>. …”

Algorithm schematic diagram of the CSM module. by Huiying Zhang (200681)

“…Experimental results show that the detection accuracy of 78% for small infrared nighttime targets, with a recall rate of 58.6%, an mAP value of 67%. and a parameter count of 20.9M for the MDCFVit-YOLO model. …”

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

HV comparison of QLNSGA-II and baseline algorithms on real-world multi-objective problems (RWMOP). The best value in each row is highlighted in bold. by Zhongfeng Li (3965060)

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Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

The average fitness function value of two—person zero—sum game simulation results. by Yufeng Meng (7504928)

Functions and classification of computer data mining technology. by Huijun Chen (148680)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

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Probability density of the cumulative number <i>C</i> of infections for different system sizes <i>N</i> at their critical vaccination doses with the random vaccination strategy as... by Yannick Feld (16520678)

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Different mathematical formalization of model fairness. TPR: True Positive Rate; FPR: False Positive Rate; FNR: False Negative Rate; TNR: True Negative Rate; PPV: Positive Predicti... by Marine Hoche (20896647)

Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Fixed-dimensional multimodal reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”