Search alternatives:
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
final based » linac based (Expand Search), final breed (Expand Search), animal based (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
final based » linac based (Expand Search), final breed (Expand Search), animal based (Expand Search)
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61
Pre-optimization iteration process.
Published 2025“…Finally, the correctness of the model is analyzed through numerical experiments, and the effectiveness of the proposed algorithm, the joint distribution strategy, and the disruption event processing idea of combining immediate processing and scheduled batch processing is analyzed. …”
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Comparative discussion based on routing.
Published 2023“…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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66
Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
Published 2022“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”
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67
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68
New building interior space layout model flow.
Published 2025“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”
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69
DataSheet1_Genetic Algorithm-Based Optimization of Curved-Tube Nozzle Parameters for Rotating Spinning.docx
Published 2021“…<p>This paper proposes an optimization paradigm for structure design of curved-tube nozzle based on genetic algorithm. …”
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70
DataSheet1_Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.docx
Published 2022“…In this paper, a genetic algorithm-based optimization method for ladle refractory lining structure is proposed. …”
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71
The parameters setting of all algorithms.
Published 2023“…Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. …”
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72
Optimization results of structural parameters.
Published 2025“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”
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73
INFO-KELM optimization results.
Published 2025“…To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. …”
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74
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75
Genetic algorithm flowchart.
Published 2024“…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. …”
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76
Results of KNN.
Published 2024“…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. …”
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77
Comparison of key techniques in their literature.
Published 2024“…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. …”
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78
SHAP analysis mean value.
Published 2024“…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. …”
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79
Proposed methodology.
Published 2024“…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. …”
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80
SHAP analysis.
Published 2024“…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. …”