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Titlebook: Artificial Intelligence and Computational Intelligence; Second International Hepu Deng,Duoqian Miao,Fu Lee Wang Conference proceedings 2011

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樓主: Stubborn
21#
發(fā)表于 2025-3-25 03:59:14 | 只看該作者
22#
發(fā)表于 2025-3-25 08:08:23 | 只看該作者
23#
發(fā)表于 2025-3-25 14:54:24 | 只看該作者
N. Regina Hershey,S. Bijoy Nandanclassical mutation operator only, will be accepted in low probability. This strategy can improve diversification and keep DE algorithm from premature convergence. Simulation experiments were carried on typical benchmark functions, and the results show that fine evaluation strategy can improve the performance of DE algorithm remarkably.
24#
發(fā)表于 2025-3-25 17:48:06 | 只看該作者
Albert Mnatsakanyan,Marina Pobegaylotics of train flows and dealing with the emergencies of the railroad net. Experimental results show that the hybrid model is proper and efficient for distributed simulation of train-group of the railroad net.
25#
發(fā)表于 2025-3-25 23:22:33 | 只看該作者
26#
發(fā)表于 2025-3-26 01:09:38 | 只看該作者
Differential Evolution Algorithm with Fine Evaluation Strategy for Multi-dimensional Function Optimiclassical mutation operator only, will be accepted in low probability. This strategy can improve diversification and keep DE algorithm from premature convergence. Simulation experiments were carried on typical benchmark functions, and the results show that fine evaluation strategy can improve the performance of DE algorithm remarkably.
27#
發(fā)表于 2025-3-26 04:58:41 | 只看該作者
28#
發(fā)表于 2025-3-26 09:11:45 | 只看該作者
0302-9743 from the Second International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011, held in Taiyuan, China, in September 2011. .The total of 265 high-quality papers presented were carefully reviewed and selected from 1073 submissions. The topics of Part I covered are: appl
29#
發(fā)表于 2025-3-26 15:45:33 | 只看該作者
Martin Bullinger,Edith Elkind,J?rg Rotheompensation. Tustin model is a nonlinear friction one, and the friction properties can be reflected better with four friction parameters. In this paper, we use genetic algorithms for parameter identification of these parameters, and its search capabilities and more robust than traditional algorithms, to be more accurate recognition results.
30#
發(fā)表于 2025-3-26 17:46:40 | 只看該作者
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