بدائل البحث:
design optimization » bayesian optimization (توسيع البحث)
yet optimization » art optimization (توسيع البحث), lead optimization (توسيع البحث), path optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
yet optimization » art optimization (توسيع البحث), lead optimization (توسيع البحث), path optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
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The flowchart of the proposed algorithm.
منشور في 2024"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"
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The Pseudo-Code of the IRBMO Algorithm.
منشور في 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. …"
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IRBMO vs. meta-heuristic algorithms boxplot.
منشور في 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. …"
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IRBMO vs. feature selection algorithm boxplot.
منشور في 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. …"
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Proposed Algorithm.
منشور في 2025"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"
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The statistical description of the original data set of the patients (<i>n</i> = 162).
منشور في 2025الموضوعات: -
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The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
منشور في 2025الموضوعات: -
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Parameter settings of the comparison algorithms.
منشور في 2024"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"