Policy network (<i>θ</i>′) of SAM to accommodate PPO algorithm. by Zhenhan Huang (12107002)

Comparing runtimes and the number of minimized conformations between fries/<i>EWAK</i>* and <i>BBK</i>* for a variety of designs. by Anna U. Lowegard (8051597)

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Convergent curves of EOSA on standard benchmark functions over 1, 50, 100, 200, 300, 400 and 500 epochs. by Tehnan I. A. Mohamed (16846175)

Data_Sheet_1_SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.docx by Ekaterina Mikhaylets (17865407)

“…<p>The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). …”

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform by Zhehong Ai (17937324)

Subjects: “…customized multiobjective functions…”

Experimental design. by Anis Najar (9745561)

S1 Data - by Takashi Naka (272391)

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A synthetic biology approach for the design of genetic algorithms with bacterial agents by A. Gargantilla Becerra (8734841)

“…To this end, we designed a genetic algorithm, which we have named BAGA, illustrating its utility solving simple instances of optimization problems such as function optimization, 0/1 knapsack problem, Hamiltonian path problem. …”

The recurrent T3D algorithm. by Neythen J. Treloar (8698251)

Experiment 2D and 5D: Progressive Sample Scaling Algorithm To Solve Many-Affine BBOB Functions. by Boris Almonacid (4110337)

“…</p><p dir="ltr"><b>Objectives</b></p><ul><li>Solve Many-Affine BBOB Functions using a Deterministic Algorithm.</li></ul><p dir="ltr"><b>Limitations</b></p><ul><li>This algorithm is designed for Many-Affine BBOB problems of 2 and 5 dimensions.…”

Average fitness and standard deviation of each algorithm across the seven engineering design problems. <i>Ave</i> indicates average fitness, <i>Std</i> indicates standard deviation... by Junhao Wei (6816803)

WSN optimized by different algorithms. by Qingwen Meng (156386)

“…This mechanism ensures that individuals that fail to update have a certain probability of being retained in the next generation population, while guaranteeing that the current global optimal solution remains unchanged, thereby accelerating the algorithm’s convergence. The ASBOA algorithm was evaluated using the CEC2017 and CEC2022 benchmark test functions and compared with other algorithms (such as PSO, GWO, DBO, and CPO). …”

Compare algorithm parameter settings. by Qingwen Meng (156386)

“…This mechanism ensures that individuals that fail to update have a certain probability of being retained in the next generation population, while guaranteeing that the current global optimal solution remains unchanged, thereby accelerating the algorithm’s convergence. The ASBOA algorithm was evaluated using the CEC2017 and CEC2022 benchmark test functions and compared with other algorithms (such as PSO, GWO, DBO, and CPO). …”

Wilcoxon’s test. by Jin Ding (11522)

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Individual expression tree. by Jin Ding (11522)

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Parameters setting of experiment. by Jin Ding (11522)

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GP problems. by Jin Ding (11522)

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MS-GEP genetic operation. by Jin Ding (11522)

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The test error of RF and MS-GEP. by Jin Ding (11522)

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The K-expression of CP-EE model. by Jin Ding (11522)

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