Search alternatives:
codon optimization » wolf optimization (Expand Search)
based optimization » whale optimization (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
tasks based » task based (Expand Search), cases based (Expand Search)
codon optimization » wolf optimization (Expand Search)
based optimization » whale optimization (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
tasks based » task based (Expand Search), cases based (Expand Search)
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Proposed Algorithm.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Comparisons between ADAM and NADAM optimizers.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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The Pseudo-Code of the IRBMO Algorithm.
Published 2025“…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. …”
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IRBMO vs. meta-heuristic algorithms boxplot.
Published 2025“…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. …”
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IRBMO vs. feature selection algorithm boxplot.
Published 2025“…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. …”
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Table5_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Image2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Table1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Image1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Image3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Table3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Table4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Table2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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Image4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG
Published 2023“…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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An Example of a WPT-MEC Network.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Related Work Summary.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Simulation parameters.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Training losses for N = 10.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
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Normalized computation rate for N = 10.
Published 2025“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”