The optimization result of 10 benchmark functions for different algorithms (D = 10). by Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 30). by Yu Li (183836)

The result of CEC2010 standard functions for different algorithms (D = 30). by Yu Li (183836)

The optimization result of 10 benchmark functions for different algorithms (D = 100). by Yu Li (183836)

The convergence curves of the function in different dimensions. by Yu Li (183836)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

Subjects:

Functional effect prediction of FUS mutations by ten nsSNP prediction algorithms. by L. F. S. Bonet (11502082)

Convergence curves of multimodal functions <i>f</i>₇. by Yu Li (183836)

Convergence curves of unimodal functions <i>f</i>₂. by Yu Li (183836)

Convergence curves of unimodal functions <i>f</i>₂. by Yu Li (183836)

Convergence curves of multimodal functions <i>f</i>₇. by Yu Li (183836)

Coarse-fine optimization algorithm. by Chao Ma (207385)

“…The improved gradient extraction method combines the Scale Invariant Feature Transformation (SIFT) algorithm to form a new multi-scale image sharpness evaluation function, SIFT Quad-Tenen. …”

Types of training algorithms. by Caixia Yan (6647072)

Parameters of the test function. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Flowchart of system algorithm. by Syed Zahurul Islam (9635163)

Heat maps of mean scores over 50 datasets of the algorithms as a function of relative noise and number of samples. by Yuanchen Zhao (12905580)

Subjects:

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

Subjects:

Objective function value statistics. by Chao Li (145513)

Comparison with the BST algorithm for the AF and CST bundles. by Thomas Durantel (20933812)

Subjects:

The iteration curve of the test function. by Jian Kong (164512)

“…The directional preference algorithm could further provide more valuable solutions on the basis of adaptive genetic algorithms. …”