Search alternatives:
codon optimization » wolf optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
binary 2 » binary _ (Expand Search), binary b (Expand Search)
2 global » _ global (Expand Search), a global (Expand Search), b global (Expand Search)
codon optimization » wolf optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
binary 2 » binary _ (Expand Search), binary b (Expand Search)
2 global » _ global (Expand Search), a global (Expand Search), b global (Expand Search)
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S and V shaped transfer functions.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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S- and V-Type transfer function diagrams.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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Collaborative hunting behavior.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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Friedman average rank sum test results.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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IRBMO vs. variant comparison adaptation data.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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Parameter settings.
Published 2024“…<div><p>Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. …”
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Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model
Published 2019“…The methodology incorporates key features of the symmetric eNRTL-SAC model structure to reduce the number of parameters and uses a hybrid of global search algorithms for parameter estimation. …”
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Thesis-RAMIS-Figs_Slides
Published 2024“…<br><br>Although the presented work was focused on 2-D binary channelized structures (geological facies), the applied principles are general and it can be extended to the characterization and recovery of other geological signals with spatial structure in under sampling contexts. …”
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Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction
Published 2020“…Subsequent Pourbaix Ir–H<sub>2</sub>O analysis shows that α-IrO<sub>3</sub> is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO<sub>2</sub>. …”
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GSE96058 information.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”
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The performance of classifiers.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”