Comparison of performance obtained by our approach with other state-of-the-art algorithms. by Sajad Mousavi (6672425)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

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Function <i>wVL</i> for evaluation of resettable bistability. by Takashi Naka (272391)

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Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …”

Data_Sheet_1_Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography.docx by Takatsugu Aihara (4433524)

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Exponent in memory kernel and intensity function. by Mariko I. Ito (5007323)

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. by Quynh-Anh Nguyen (847240)

“…Lower panel: The signal detection algorithm constructs neurometric functions using numerical data from all <i>N</i>_<i>in</i> neuronal units. …”

Hypothetical example illustrating how the <i>search</i>.<i>conv</i> function algorithm works. by Silvia Castiglione (5010581)

Boxplot of fitness in various 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.…”

Performance of PSAR algorithm with different weight coefficient <i>α</i>. by Shuang Guo (294074)

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Python scripts used to model CPU errors by Poisson distribution density mass function and to draw graph of Poisson events (needs Matplot, SciPy, PysimpleGUI and NumPy) by Santhosh Kumar Rajamani (13023678)

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.…”

Application of multi-scale community detection to subject-level and group-level resting state functional brain networks. by Arian Ashourvan (6685232)

Parameters of the genetic algorithm. by Jian Ma (170138)

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Matlab code of a function used in S2 File. by Hiroko Kato Solvang (5718250)

Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…The experimental observations reveal that the proposed DA-based image contrast enhancement produces high-quality images from its low-contrast counterparts. Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. …”

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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