The optimal solution set of HN by using different algorithms. حسب Yi Tao (178829)

الموضوعات: "…evolutionary genetic algorithm…"

Comparative statistical results obtained against benchmark functions. حسب Serdar Ekinci (15927455)

Line plots with the RI, left, TPR, centre, and TNR, right for the simulations with one replicate against the number of clusters for FRECL and the five alternative methods for simul... حسب Susana Conde (5495675)

الموضوعات:

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF حسب Minhua Guo (18392646)

The SSA based SVM objective function minimization against iterations. حسب Ali Ahmed (2567584)

gmxapi usage examples. حسب M. Eric Irrgang (4487278)

libEnsemble: New capabilities for coordinating dynamic ensembles of calculations حسب John-Luke Navarro (9024008)

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

"…Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…"

Complementary cumulative distribution functions of in degree distribution and fitted power law, lognormal and truncated power law distributions. حسب Olga Valba (10531121)

Algorithm 1. حسب Muhammad Zubair Rehman (11243478)

Benchmark functions. حسب Yun Qi (560401)

Results of the PSO-Kmeans-ANMS algorithm for the Rastrigin function. حسب Rutinaldo Aguiar Nascimento (14259594)

الموضوعات:

Results of the PSO-Kmeans-ANMS algorithm for the Rosenbrock function. حسب Rutinaldo Aguiar Nascimento (14259594)

الموضوعات:

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf حسب Santiago Hernández-Orozco (5070209)

"…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…"

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf حسب Santiago Hernández-Orozco (5070209)

"…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…"

Auto-tuning algorithm of the weight factors in the cost function. حسب Syed Umaid Ali (14486372)

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

Available functions in the utility package. حسب Raul Castañeda (13932971)

The process of function F in the improved Blowfish algorithm. حسب Sathish Kaveripakam (16940710)

Optimization results of different algorithms on benchmark functions. حسب Guangwei Liu (181992)