بدائل البحث:
function optimization » reaction optimization (توسيع البحث), formulation optimization (توسيع البحث), generation optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
based function » based functional (توسيع البحث), basis function (توسيع البحث), basis functions (توسيع البحث)
less based » lens based (توسيع البحث), lemos based (توسيع البحث), degs based (توسيع البحث)
function optimization » reaction optimization (توسيع البحث), formulation optimization (توسيع البحث), generation optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
based function » based functional (توسيع البحث), basis function (توسيع البحث), basis functions (توسيع البحث)
less based » lens based (توسيع البحث), lemos based (توسيع البحث), degs based (توسيع البحث)
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81
Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf
منشور في 2024"…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …"
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82
Fig 5 -
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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83
DataSheet1_An optimized three-dimensional time-space domain staggered-grid finite-difference method.docx
منشور في 2023"…Examining the numerical dispersion, algorithm stability and computational cost, we compare our optimized time-space domain LS-based 3D SFD method with three conventional TE-based and LS-based 3D SFD methods to illustrate and demonstrate its effectiveness and feasibility. …"
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84
Cross Validation mechanism for an RL case.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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85
Monte carlo test ranking from elitism phase.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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86
Individual #5’s action ratio, position states.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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87
RSF Components of the best five individuals.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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88
Open loop simulation.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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89
Average wind test fitness.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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90
Internal process of a policy gradient block.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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91
Training process of a DDPG individual.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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92
PbGA search phases to find the best individuals.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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93
Previous usages of DRL in solving PDG problems.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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94
Internal process of a critic gradient block.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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95
Best Individuals from the mapping phase.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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96
Table_1_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX
منشور في 2022"…In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. …"
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97
Table_2_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX
منشور في 2022"…In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. …"
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98
The_Code_for_High_Order_Analytical_Continuation
منشور في 2024"…The optimal order of the analytical continuation algorithm is contingent upon the noise level of gravity data. …"
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99
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100
Low-Order Scaling <i>G</i><sub>0</sub><i>W</i><sub>0</sub> by Pair Atomic Density Fitting
منشور في 2020"…We derive a low-scaling <i>G</i><sub>0</sub><i>W</i><sub>0</sub> algorithm for molecules using pair atomic density fitting (PADF) and an imaginary time representation of the Green’s function and describe its implementation in the Slater type orbital (STO)-based Amsterdam density functional (ADF) electronic structure code. …"