Greedy Man Optimization Algorithm (GMOA) by Hamed Nozari (20601020)

“…The algorithm introduces two unique mechanisms: MMO resistance, which prevents premature replacement of solutions, ensuring stability and diversity, and periodic parasite removal, which promotes mutation and prevents stagnation. …”

Results of average processing speed for the compared methods. by Haoyuan Yang (8630391)

Attack and defense behaviors with the sensory–motor algorithm between multiple predators and prey. by Hiroyuki Ichijo (13891153)

Fraction of selective neurons as a function of the imaging signal-to-noise ratio. by Ziqiang Wei (5953964)

PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared. by Gursimran Singh (575288)

Subjects:

R-squared comparison of test function. by Kejia Liu (5699651)

“…<div><p>The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. …”

Metapopulation model notation. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Estimates of for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

The G-clusteron requires the weight and location update rules to solve the XOR problem from arbitrary initial conditions. by Toviah Moldwin (10866708)

Highest 18 BacSD rankings as a function of detection probability using simulated data with a clutter mean of 8. by Tom Northardt (17817484)

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

Results of hyperparameter searches for neural decoding models, which varied the number of hidden layers and number of hidden units per layer in an MLP neural network. by Matthew R. Whiteway (11467528)

Fitness function curve at weight factor 0.3. by Dhulfiqar Hakeem Dhayef (17729684)

“…GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (<i>q</i> = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). …”

Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining Sub-electronvolt Error from a Restricted Open-Shell Kohn–Sham Approach by Diptarka Hait (2835971)

“…This high accuracy can be contrasted with traditional time-dependent density functional theory (TDDFT), which typically has greater than 10 eV error and requires translation of computed spectra to align with experiment. …”

Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining Sub-electronvolt Error from a Restricted Open-Shell Kohn–Sham Approach by Diptarka Hait (2835971)

“…This high accuracy can be contrasted with traditional time-dependent density functional theory (TDDFT), which typically has greater than 10 eV error and requires translation of computed spectra to align with experiment. …”

Five types of Beta functions considered. by Luan Carlos de Sena Monteiro Ozelim (16914117)

A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

“…One can expect that the combination of multi-dimensional parameters from evolutionary biology, cell biology and molecular biology on evolutionary diverse species will significantly improve the next generation of machine leaning algorithms that serve localisation (and function) predictions.…”

Improved SHA256 algorithm operation diagram. by Su Li (294188)

DE genes detected by different DEA algorithms. by Jie Xue (417504)

“…Here, we compare 6 performance metrics on both simulated and real scRNA-seq datasets to assess the adaptability of 8 DEA approaches, with a particular emphasis on how well they function under small biological replications. Our findings suggest that DEA algorithms extended from bulk RNA-seq are still competitive under small biological replicate conditions, whereas the newly developed method DEF-scRNA-seq which is based on information entropy offers significant advantages. …”