Compare the test functions. by Yanqiu Li (683621)

Evaluation of the optimization algorithm using <i>in vitro</i> microbiome reference values. by Juan Ricardo Velasco-Álvarez (16859181)

“…<p>A: Evolution of biomass fraction distribution during the cycles of adaptation enforced by the gradient descent algorithm using a value of <i>α</i> = 0.25 and <i>in vitro</i> abundance data obtained from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0290082#pone.0290082.ref021" target="_blank">21</a>]; B: Comparison of pie charts for the <i>in vitro</i> reference biomass percentage distributions (left) and the obtained with the modeling strategy based on the gradient descent algorithm (right); C: Performance functional (<i>J</i>) evolution for <i>in vitro</i> microbiome simulation.…”

The results of CEC2022 for all algorithm. by Zihao Wang (845023)

“…To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. …”

The average time slots for all algorithms. by Zihao Wang (845023)

“…To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. …”

Details of the comparison algorithm parameters. by Yan Wei (32168)

“…Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. …”

Convergence curves of different algorithms on CEC2017 functions (dim = 30,100) and CEC2022(dim = 30). by Qingwen Meng (156386)

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Top 15 hub genes in five algorithms. by Tianqi Liu (1625776)

Schematics for gmxapi operations. by M. Eric Irrgang (4487278)

The workflow used to analyze imbalanced and balanced sequences. by Solomon Shiferaw Beyene (9143178)

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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Benchmark functions used and their details. by Wei Liu (20030)

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Test to find best k-tuple algorithm settings. by Elzbieta Rembeza (5114207)

PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. by Yuxi Long (11024307)

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IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

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IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

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Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. by Raul Castañeda (13932971)

The parallel coordinates of non-dominated front obtained by each algorithm (used in the comparison) on DTLZ3 and DTLZ 7 or any function with 10 objectives. by Heba Askr (15572851)

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The performance evaluation of various image contrast enhancement approaches using test images 1. by Xiaowen Zhang (83669)

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The performance evaluation of various image contrast enhancement approaches using test images 7. by Xiaowen Zhang (83669)

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The performance evaluation of various image contrast enhancement approaches using test images 8. by Xiaowen Zhang (83669)

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