PFC value of algorithms on two datasets. by Xing Chen (140292)

A heatmap of the examined values for Jaya algorithm parameters. by Fadi K. Dib (5204807)

Comparison results on benchmark functions with traditional algorithms. by Xiaoyi Zhang (577299)

The results of 12 benchmarking functions of the best (minimum) fitness function values. by Chuang Zhang (2199421)

The results of 12 benchmarking functions in terms of the average fitness function values. by Chuang Zhang (2199421)

Objective function value statistics. by Chao Li (145513)

Location schemes and objective function values for different values of the confidence level. by Yangjun Ren (17691912)

Flowchart of the algorithm. by Xiaowen Zhang (83669)

Subjects:

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Convergence curves of various algorithms under test functions. by Jiayuan Wang (3765376)

Controlling parameter values of the tested algorithms. by WanRu Zhao (18980374)

Flowchart of the proposed algorithm. by Heewon Chung (2881286)

Subjects:

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. …”

RMSE values of different algorithms for different bands in the "Kherson" region. by YueSheng Jiang (19267984)

RMSE values of different algorithms for different bands in the "PY" region. by YueSheng Jiang (19267984)

Results of proposed algorithms and algorithms in [9] for 18 instances. by Jin Zhang (53297)

Results of searching performance of the improved NAG algorithm on Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

Comparison of benchmark function results between improved algorithms. by Xiaoyi Zhang (577299)

Mean values of different benchmark functions. by Jalal- ud-Din (21474307)

“…Proposed parent-centric real-coded crossover operators increase the precision and robustness of GAs, which is confirmed by empirical results on testing constrained and un-constrained benchmark functions having different complexity levels. MGGX parent-centric real-coded crossover operator performs best in 20 out of 36 mean values cases and achieves the lowest standard deviation values in 21 out of 36 cases. …”