Hyperparameter optimization results. by Minan Tang (20389941)

“…Aiming at the problem of power quality disturbance detection and classification, this paper proposes a novel algorithm based on fast S-transform and crested porcupine optimizer (CPO) optimized CNN. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…This work develops and compares seven Gaussian process Bayesian optimization (GPBO) methods for calibrating nonlinear models. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…This work develops and compares seven Gaussian process Bayesian optimization (GPBO) methods for calibrating nonlinear models. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…This work develops and compares seven Gaussian process Bayesian optimization (GPBO) methods for calibrating nonlinear models. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

“…This work develops and compares seven Gaussian process Bayesian optimization (GPBO) methods for calibrating nonlinear models. …”

Optimized system structure. by Xutao Liu (13006965)

“…Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. …”

Description of optimal model parameters. by Libo Liu (552605)

Fitness curves for each algorithm. by Minan Tang (20389941)

“…Aiming at the problem of power quality disturbance detection and classification, this paper proposes a novel algorithm based on fast S-transform and crested porcupine optimizer (CPO) optimized CNN. …”

Genetic algorithm flowchart. by Wenguang Li (6528113)

“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”

Final solution set for overspeed protection for gas turbine problem (termination condition is 500 iteration). by Heba Askr (15572851)

Algorithm convergence diagram. by Yanqiu Li (683621)

“…<div><p>The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. …”

Illustration of final objective function values consistency and possible objective function landscapes. by Fabian Fröhlich (848026)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

Final solution set for a series-parallel system with five subsystems (termination condition is 500 iteration). by Heba Askr (15572851)

MAE, R-squared, RMSE & SMAPE of the final model. by Gayatree Nayak (15426871)

The Search process of the genetic algorithm. by Wenguang Li (6528113)

“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”

Cardiac Hypertrophy Computer-based model (CHCM) and the electrical LVH phenotypes. by Fernando De la Garza Salazar (11772628)

Subjects:

Hyperparameter optimization results. by Jiayue Zhang (7832669)

“…In this study, the hybrid model CMNS-YOLO, which combines the crawfish optimization algorithm with the MNS-YOLO model, is proposed to achieve the ultimate detection accuracy. …”

Simplified algorithm for reliability sensitivity analysis of structures: A spreadsheet implementation by Mahdi Shadab Far (6439454)

“…<div><p>An important segment of the reliability-based optimization problems is to get access to the sensitivity derivatives. …”

Genetic algorithm iteration data chart. by Wenguang Li (6528113)

“…Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. …”