Reward function related parameters. by Honglei Pang (22693724)

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Genetic algorithms can be applied to higher-dimensional design problems. by Lu Hong (399882)

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An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Verification of the in-line PS function. by Raul Castañeda (13932971)

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

GELU function. by Yuanzhou Zheng (15195249)

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. …”

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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MOEA/D-ND algorithm flowchart. by Jun Sun (48981)

“…At the same time, in the actual working bandwidth, the optimized power amplifier saturation efficiency reaches 51.5%~61.9%, and the efficiency at 6dB power backoff is about 44.4%~56.5%. Overall, the algorithm proposed in the study is effective in optimizing power amplifiers and their matching networks, effectively solving the problem of insufficient efficiency in low power modes in traditional designs.…”

Performance comparison in CP-EE model (a) SVR model (b) BPNN model (c) RF model (d) MS-GEP model. by Jin Ding (11522)

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Evolutionary processes on special functions (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP. by Jin Ding (11522)

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

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Population diversity comparison on all function (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP. by Jin Ding (11522)

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Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…Inertia was calculated using the scikit learn module in Python. B) Resulting cluster distance from hierarchical clustering to averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

RMSE comparison in all test function (a) F1 (b) F2 (c) F3 (d) F4 (e) F5 (f) F6 (g) F7 (h) F8 (i) F9 (j) F10. by Jin Ding (11522)

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Genetic algorithm in the design of soybean silos for airflow homogenization by Daniel Petravicius (14820463)

“…An optimization model that considers the limited and discrete nature of the air inlets, in addition to the geometric characteristics of the storage bin, could produce results more easily applicable results for the design of new grain storage bins. The objective of this study was to parameterize and to apply the Darcy-Forchheimer model for simulating airflow in soybean grain mass as function of grain layer height, and develop an artificial intelligence method based on genetic algorithm for optimizing grain storage bin dimensions and air inlet configurations to obtain a more homogeneous airflow in the grain mass. …”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

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