Convergence curve of others functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Convergence curve of multimodal functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Box plot of the ADGWDTO and competing algorithms for the seven benchmark functions. by El-Sayed M. El-kenawy (14581088)

Convergence curve of multimodal functions compared with the PSO family algorithms. by Yong Gao (29929)

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…Python modules to harness this second strategy are freely available at <a href="https://github.com/pymodproject/altmod" target="_blank">https://github.com/pymodproject/altmod</a>. …”

Algorithm used in the model to automatically estimate the parameters of LD pharmacodynamics, via minimization of a cost function. by Mauro Ursino (144905)

Predicted body weights (kg) as a function of time (age in weeks) obtained from MARS algorithm. by Turgay Şengül (12801990)

“…<p>Predicted body weights (kg) as a function of time (age in weeks) obtained from MARS algorithm.…”

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. by Mikołaj Biesaga (17816940)

Power flow and cost optimization with Improved PSO algorithm (a). by Muhammad Ahmad Iqbal (11851338)

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Loss function as a function of a number of iterations. by Kiyoshiro Okada (10711332)

Algorithm for constructing Boot-t CIs. by Qin Gong (118801)

“…Next, we select the gamma distribution as the prior distribution and apply the Lindley approximation algorithm to calculate `estimates of Shannon entropy and Rényi entropy under different loss functions including Linex loss function, entropy loss function, and DeGroot loss function respectively. …”

The parameters of algorithms. by Le Huu Binh (18340233)

“…However, solving the RNP problem in WMN is difficult because it is NP-hard. As a result, this problem can only be solved using approximate optimization algorithms such as heuristics and meta-heuristics. …”

Compare the test functions. by Yanqiu Li (683621)

Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

Subjects:

Improved NSGA-II algorithm flowchart. by Ying Wang (11406)

“…The upper objective function needs to be minimized, while the lower objective function needs to be maximized. …”

Specific information on the 7 benchmark functions. by M. A. JiMing (19207527)

Adding a new function module. by Yukako Yamane (9034133)

Ms.FPOP: A Fast Exact Segmentation Algorithm with a Multiscale Penalty by Arnaud Liehrmann (10970682)

“…This penalty was proposed by Verzelen et al. and achieves optimal rates for changepoint detection and changepoint localization in a non-asymptotic scenario. Our proposed algorithm, Multiscale Functional Pruning Optimal Partitioning (Ms.FPOP), extends functional pruning ideas presented in Rigaill and Maidstone et al. to multiscale penalties. …”

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

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