Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 30. by Haiming Du (6691646)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 50. by Haiming Du (6691646)

The image of the Aekley function. by Zhengyu Xu (8550660)

Face matching performance as a function of survey condition for Study 2 (threshold = 0.5). by Daniela Barragan (13146762)

Face matching performance as a function of test condition for Study 1 (threshold = 0.5). by Daniela Barragan (13146762)

Multimodal test functions. by HaoCheng Wang (6089567)

Unimodal test functions. by HaoCheng Wang (6089567)

Results of 7 benchmarking functions in 50 dimensions. by M. A. JiMing (19207527)

Results of 7 benchmarking functions in 30 dimensions. by M. A. JiMing (19207527)

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Feature matrix implemented by a hash sequence function. by Saqib Ali Nawaz (8934473)

ANOVA tests for Benchmark functions. by Yu Liu (6938)

“…<div><p>The competition of tribes and cooperation of members algorithm (CTCM) is a novel swarm intelligence algorithm, which increases the diversity of the population to a certain extent through tribal competition and member cooperation mechanisms. …”

The Wilcoxon results for Benchmark functions. by Yu Liu (6938)

“…<div><p>The competition of tribes and cooperation of members algorithm (CTCM) is a novel swarm intelligence algorithm, which increases the diversity of the population to a certain extent through tribal competition and member cooperation mechanisms. …”

Convergence graphs of Benchmark functions. by Yu Liu (6938)

“…<div><p>The competition of tribes and cooperation of members algorithm (CTCM) is a novel swarm intelligence algorithm, which increases the diversity of the population to a certain extent through tribal competition and member cooperation mechanisms. …”

Detailed information of benchmark functions. by Guangwei Liu (181992)

“…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”

<b></b>Magnetic Kernels: A Theoretical and Algorithmic Framework for the P vs NP Problem by DURGHAM QARALLEH (21904172)

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. …”

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)