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Titlebook: Computational Intelligence and Intelligent Systems; 9th International Sy Kangshun Li,Wei Li,Yong Liu Conference proceedings 2018 Springer N

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41#
發(fā)表于 2025-3-28 14:41:47 | 只看該作者
42#
發(fā)表于 2025-3-28 20:30:48 | 只看該作者
Bacterial Foraging Algorithm Based on Reinforcement Learning for Continuous Optimizationsi.e., cell-to-cell crossover) to dynamically balance exploration and exploitation during the search process. Then the proposed algorithm is evaluated against several state-of-the-art algorithms on a set of continuous benchmark instances. Experimental results verify the significant superiority of the proposed algorithm.
43#
發(fā)表于 2025-3-29 02:27:06 | 只看該作者
44#
發(fā)表于 2025-3-29 06:36:48 | 只看該作者
Multi-objective Firefly Algorithm Guided by Elite Particletal results show that the proposed algorithm is superior to the MOPSO, MOEA/D, PESA-II, NSGA-III algorithm on the GD, SP, MS and other quantitative indexes for the seven classic test functions, and the proposed algorithm is an effective method for multi-objective optimization.
45#
發(fā)表于 2025-3-29 10:24:47 | 只看該作者
Conference proceedings 2018angzhou, China, in November 2017.The 101 full papers presented in both volumes were carefully reviewed and selected from 181 submissions. This first volume is organized in topical sections on neural networks and statistical learning: neural architecture search,?transfer of knowledge;?evolutionary mu
46#
發(fā)表于 2025-3-29 14:23:35 | 只看該作者
47#
發(fā)表于 2025-3-29 19:36:40 | 只看該作者
48#
發(fā)表于 2025-3-29 21:45:03 | 只看該作者
49#
發(fā)表于 2025-3-30 00:51:21 | 只看該作者
50#
發(fā)表于 2025-3-30 06:56:12 | 只看該作者
Ontology Sparse Vector Learning Algorithmaper two experiment results showed algorithm by using squared loss function to express the error term, Finally, two experiment results showed the effective of our ontology sparse vector learning algorithm for ontology similarity computation in specific engineering applications.
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