Performance of tpopt function for finding <i>T</i>-optimal designs. by Ray-Bing Chen (700703)

Iteration curves of the algorithms in I-beam design problem. by Junhao Wei (6816803)

Comparison of algorithm performance in ZDT1 and ZDT2 function tests. by Tao Dong (15551)

“…<p>Comparison of algorithm performance in ZDT1 and ZDT2 function tests.…”

Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

Comparative statistical performance of various algorithms in minimizing the <i>F</i> cost function. by Davut Izci (15927452)

Subjects:

Performance of KLopt.lnorm function for finding <i>KL</i>-optimal designs. by Ray-Bing Chen (700703)

The algorithm is designed to identify patterns in the treatment dataset. by Belén Otero-Carrasco (21262360)

“…<p>This process involves two key functions. First, from lines 1 to 8, the algorithm identifies amino acids that meet the occurrence threshold, starting with patterns of length 1. …”

Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems by Dongge Lei (6836915)

Subjects: “…Imperialist competition algorithm…”

Performance comparison of algorithms in ZDT5 and ZDT6 function tests. by Tao Dong (15551)

Comparison of algorithm performance in ZDT3 and ZDT4 function tests. by Tao Dong (15551)

Approximate energy consumed per particles in <i>nano Joules</i> (<i>nJ</i>) for various computation platforms and algorithms (A: first algorithm, B: second algorithm, C: third algo... by Abedalmuhdi Almomany (18547669)

Algorithm pseudocode. by Cheng-jie Chen (22272090)

Subjects:

Parameters <i>p</i> identified by the APLM algorithm. by HaiYan Jiang (4455199)

Subjects:

Solid lines present trend curves for normalized rolling algorithmic complexity and dashed lines depict the average rolling algorithmic complexity as a function of experimental cond... by Mikołaj Biesaga (17816940)

Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms. by Yao Peng (1928524)

“…<p>Objective functions (i.e., RMSEs) and time of optimization for different population sizes of the used algorithms.…”

Compare the test functions. by Yanqiu Li (683621)

Standard functions to test. by Kemal Gökhan Nalbant (18858009)

Experiment 1b: Evaluating modularity-based consensus clustering pipelines on the algorithm design datasets as a function of the mixing parameter. by Yasamin Tabatabaee (18095491)

Flowchart of the general algorithm for one trial. by Junying Wang (509009)

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …”