“trend+var” algorithm. حسب Roman Olson (6577004)

The ALO algorithm optimization flowchart. حسب Wenjing Wang (181404)

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The IALO algorithm solution flowchart. حسب Wenjing Wang (181404)

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Parameters of trained algorithms. حسب Swathi Anil (17382903)

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Performance of algorithms compared to a smaller <i>RNN.</i> حسب Swathi Anil (17382903)

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Detailed information of benchmark functions. حسب Guangwei Liu (181992)

"…In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. …"

Hyperparameter settings of the algorithm 1. حسب Jin Xu (31283)

"…Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). …"

Solid lines present trend curves for normalized rolling algorithmic complexity and dashed lines depict the average rolling algorithmic complexity as a function of experimental conditions. حسب Mikołaj Biesaga (17816940)

Algorithm pseudocode. حسب Cheng-jie Chen (22272090)

"…In order to improve the clarity and visual effect of product appearance design, a visual edge feature enhancement method for product appearance design images based on an improved Retinex algorithm is proposed. …"

CPU runtime of each algorithm: (a) Running Time Comparison of Different Algorithms on F1-F10 and (b) Running Time Comparison of Different Algorithms on F11-F20. حسب Ruyi Dong (9038174)

Mean training time of different algorithms. حسب 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.…"

CEC2019 benchmark functions. حسب Guangwei Liu (181992)

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The signal detection algorithm for constructing a neurometric function (the probability of segregation as a function of time) generates acceptable buildup fits at <i>DF</i> = 1, 3, 6, 9. حسب Quynh-Anh Nguyen (847240)

"…Parameters <i>N</i>_<i>in</i> and <i>C</i>_<i>th</i> are chosen to yield SEM similar to those observed in the spike count data [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>, (Fig.3A in Ref)] and to yield the least-squares error of the experimental buildups (dashed, extracted from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>, (Fig.4 in Ref)] and the computer-simulated neurometric functions (solid) for <i>DF</i> = 1, 3, 6, 9. …"

(a-c) Sampling event times with an intensity function . حسب Gerrit Großmann (9585147)

Continuous Probability Distributions generated by the PIPE Algorithm حسب LUIS G.B. PINHO (14073372)

"…<div><p>Abstract We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. …"

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application حسب Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application حسب Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application حسب Yizheng Lyu (17346506)

Eight commonly used benchmark functions. حسب Guangwei Liu (181992)

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Performance comparison of mainstream algorithms. حسب Junjie Lu (160350)

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