Experiment 2: Evaluating modularity-based consensus clustering pipelines on synthetic networks based on clustered real-world networks. by Yasamin Tabatabaee (18095491)

The Pareto-optimal fronts on the random network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

Subjects: “…multiobjective evolutionary algorithm…”

The Pareto-optimal fronts on the regular network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

Subjects: “…multiobjective evolutionary algorithm…”

APL, ACC and ACC /APL of the optimal networks with different goals and number of added edges. by Ruochen Zhang (3434996)

Subjects: “…multiobjective evolutionary algorithm…”

A schematic diagram of the distribution of base stations in a city shows that the distribution of base stations is not even, showing a trend of being dense inside and sparse outsid... by Xiaorui Wu (8322705)

Adopted parameter ranges and the obtained best parameters via different algorithms. by Erdal Eker (19251018)

Performance comparison of four optimization methods. by Xuexing Du (20624042)

“…<p>(A) Comparison of running time (in minutes) among four optimization methods for solving the same task in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012318#pcbi.1012318.g004" target="_blank">Fig 4E</a>: parallel simulation-based inference (P-SBI), dual annealing (DA), differential evolution (DE), and particle swarm optimization (PSO). …”

Optimal ovarian cancer BNs learned by CausNet and CausNet-partial. by Nand Sharma (21519845)

Subjects:

Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Statistical performance evaluation of algorithms and p-values obtained from Wilcoxon’s test. by Erdal Eker (19251018)

Percentage of edges added within the same community in the optimal benchmark networks with different mixing parameter and goals. by Ruochen Zhang (3434996)

Subjects: “…multiobjective evolutionary algorithm…”

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. …”

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

The hyperparameters selected for CLPREM, as well as other settings of the model, were determined based on experience and testing. by Xiaorui Wu (8322705)

Wrapper-based feature selection methods. by Xiaohua Zeng (793632)

Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Flow chart of particle swarm algorithm. by Nour Eldeen Mahmoud Khalifa (19259450)

“…</p><p>Method</p><p>In this paper, a deep fusion model based on whale optimization and an artificial neural network for Arabian date classification is proposed. …”

ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Energy consumption at each joint for different methods. by Hedyeh Jafari (9667127)

Subjects:

Evolution of changing weights in the optimization of the MPC controller. by Hedyeh Jafari (9667127)

Subjects: