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

Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement by Antoine Falisse (6061601)

“…<div><p>Algorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. …”

Algorithm of the PbGA search for the optimal PbF. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Multiobjective Tuning and Performance Assessment of PID Using Teaching–Learning-Based Optimization by Wei Zhang (405)

“…The numerical examples of CPA problems show that the algorithm can generate better MOV than existing methods with less calculation time. …”

Experiment 4: Evaluating CPM-based clustering methods on ring-of-cliques networks. by Yasamin Tabatabaee (18095491)

Experiment 4: Evaluating modularity-based clustering methods on tree-of-cliques networks. by Yasamin Tabatabaee (18095491)

Experiment 4: Accuracy of modularity-based consensus clustering methods on ring-of-cliques networks of varying sizes. by Yasamin Tabatabaee (18095491)

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

“…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. …”

Loss function curve. by Xiangqian Xu (17310895)

“…Finally, the loss function CIoU of YOLOv7 is optimized to EIoU loss function to accelerate the convergence speed of the model. …”

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

S1 Dataset - by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of ACC on the random network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Parameters in the experiment. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of APL on the random network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of ACC on the regular network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Statistical tests of APL on the regular network. by Ruochen Zhang (3434996)

“…A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. …”

Experiment 5: Clustering networks that are only partially clusterable. by Yasamin Tabatabaee (18095491)

Optimal configuration of RC frames considering ultimate and serviceability limit state constraints by Marcela Alejandra Juliani (10449275)

“…Structural analyses are performed by using the MASTAN2 software, taking into account geometric nonlinearities and a simplified physical nonlinearity method. The objective function considers the cost of concrete, reinforcement and formwork, and the optimization problems are solved by genetic algorithms. …”

Genetic algorithm meta-parameters. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

The entity relationship of DDPG algorithm. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”