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algorithm which » algorithm within (Expand Search)
which function » beach function (Expand Search)
algorithm wave » algorithm based (Expand Search), algorithm a (Expand Search), algorithm ai (Expand Search)
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wave function » rate function (Expand Search), a function (Expand Search), gene function (Expand Search)
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A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases
Published 2025Subjects: -
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The SSIM for the different algorithms.
Published 2024“…The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. …”
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Ridge wave transformation flowchart.
Published 2024“…The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. …”
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Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined.
Published 2024“…(C) We use the synthetic data as input for three families of regression-based algorithms: the EQO of Ref. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.ref026" target="_blank">26</a>] (which groups species into two groups), and two families we call K-means and Metropolis (see text), which can return any specified number of groups. …”
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Block ridge wave denoising flowchart.
Published 2024“…The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. …”
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Algorithm parameter setting.
Published 2023“…Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.…”
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Algorithm parameter setting.
Published 2023“…Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.…”
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Membership function of each target.
Published 2023“…Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.…”
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Multinode Multi-GPU Two-Electron Integrals: Code Generation Using the Regent Language
Published 2022Subjects: -
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Convergence curve of each test function.
Published 2023“…Experimental results show that the PSCACO algorithm proposed in this paper is compared with MOPSO, CACO and NSGA-II algorithms, and the convergence effect of the algorithm is concluded to be more effective to verify the effectiveness and feasibility of chaotic particle ant colony algorithm for solving multi-objective functions, which proposes a new feasible solution for the supply chain management.…”
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