Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

gmxapi usage examples. by M. Eric Irrgang (4487278)

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

First-order lag averaging filtering algorithm. by Shaohua Fu (10573209)

“…The system improves the algorithm of the data collected by the sensor, and proposes an improved first-order lag average filtering algorithm. …”

Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

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Operation curve comparison among the EITO algorithms. by Yunhu Huang (21402795)

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Operation curve comparison among the EITO algorithms. by Yunhu Huang (21402795)

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Distribution of FEV1/FVC in pulmonary function tests with discrepant interpretations between the algorithm and the clinicians. by Yuh-Chin T. Huang (17867207)

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

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

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Mean training time of different algorithms. by Wei Liu (20030)

“…The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Algorithm ranking under different dimensions. by Wei Liu (20030)

“…The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

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One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

All thresholding algorithms show a sharp decrease in MSE after just a few batch iterations. by Ilias Rentzeperis (10215602)

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Overview of different metaheuristic algorithms used in PV parameter estimation and their corresponding full names and acronyms. by Ziad M. Ali (12516532)

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(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

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Evaluation of the optimization algorithm using mock microbiome reference values. by Juan Ricardo Velasco-Álvarez (16859181)

“…<p>A: Evolution of fraction community distribution concerning the cycles of adaptation enforced by the gradient descendant algorithm using a value of <i>α</i> = 0.125 and a mock microbiome composition; B: Comparison of pie charts for the mock reference biomass percentage distributions (left) and the obtained with the modeling strategy based on the gradient descent algorithm (right). …”

Evaluation of the optimization algorithm using clinical microbiome reference values. by Juan Ricardo Velasco-Álvarez (16859181)

“…<p>A: Evolution of biomass fraction distribution for the cycles of adaptation enforced by the gradient descendant algorithm using a value of <i>α</i> = 0.125 and a microbiome composition from Mexican patients published by [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0290082#pone.0290082.ref020" target="_blank">20</a>]; B: Comparison of pie charts for the clinical 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 using clinical data from healthy Mexican patients for the simulation.…”

Genetic Algorithm parameter search. by Elishai Ezra-Tsur (11888717)