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.…”

Algorithm pseudocode. by Cheng-jie Chen (22272090)

Subjects:

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

“…<p>Performance comparison of algorithms in ZDT5 and ZDT6 function tests.</p>…”

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

“…<p>Comparison of algorithm performance in ZDT3 and ZDT4 function tests.…”

Compare the test functions. by Yanqiu Li (683621)

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

“…<p>Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms.…”

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

Subjects:

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

Subjects: “…Imperialist competition algorithm…”

Performance of tpopt function for finding <i>T</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. …”

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)

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. …”

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)

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

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

Subjects:

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.…”

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

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. …”