Optimized process of the random forest algorithm. by Hongxia Li (493545)

“…Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. …”

Schematic diagram of Latin hypercube sampling. by Guilin Yang (583364)

Image_1_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_5_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_2_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_3_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

Image_4_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

The flowchart of Algorithm 2. by Jing Xu (15337)

“…For the former, it is further divided into two sub-problems according to the stochastic nature of passenger no-show behavior, which is optimized iteratively. Finally, the effectiveness of the proposed model and algorithm is evaluated through numerical studies. …”

Parameter values of the compared algorithms. by Huimin Lu (4434682)

“…Finally, we proposed an escape Coati Optimization Algorithm (eCOA) for global optimization to enhance classification performance. …”

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

Improved random forest algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Sampling points during the 6th respiration. by Feng Gao (3548)

Image2_An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm.TIF by Hao Bai (555291)

“…In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. …”

Image1_An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm.TIF by Hao Bai (555291)

“…In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. …”

Image2_An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm.TIF by Hao Bai (555291)

“…In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. …”

Image1_An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm.TIF by Hao Bai (555291)

“…In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. …”

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

Algorithm flow of the GA-BPNN model. by Xiying Wang (4859998)

“…Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency. To address these problems, a genetic algorithm (GA) is adopted for optimizing the BPNN, and the EGA-BPNN model is used to predict irrigation flow in agricultural fields. …”

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. …”