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Titlebook: Computational Intelligence; International Joint Kurosh Madani,António Dourado Correia,Joaquim Fili Conference proceedings 2015 Springer In

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11#
發(fā)表于 2025-3-23 09:48:49 | 只看該作者
12#
發(fā)表于 2025-3-23 16:00:11 | 只看該作者
https://doi.org/10.1007/978-1-4757-6596-0 will focus on the impact of forbidding some of the rule-type or their combination in the development of the entire family of PM colonies with such restriction and we will also look into the impact of restrictions on the generative power of PM colonies.
13#
發(fā)表于 2025-3-23 18:33:51 | 只看該作者
Prediction and Filtering of Processes,P, and such fuzzy goals are quantified by eliciting the corresponding membership functions. Using the fuzzy decision, such two kinds of membership functions are integrated. In the integrated membership space, the satisfactory solution is obtained from among a Pareto optimal solution set through the interaction with the decision maker.
14#
發(fā)表于 2025-3-24 01:36:55 | 只看該作者
Application of Base Learners as Conditional Input for Fuzzy Rule-Based Combined System results show that for a given large size of the base learners pool, the outputs of some of them can be useful in the antecedent parts to produce accurate ensembles, while at the same time other more accurate members of the same pool contribute in the consequent part.
15#
發(fā)表于 2025-3-24 05:38:17 | 只看該作者
On Decidability Results in Point Mutation Colonies with Restricted Rules will focus on the impact of forbidding some of the rule-type or their combination in the development of the entire family of PM colonies with such restriction and we will also look into the impact of restrictions on the generative power of PM colonies.
16#
發(fā)表于 2025-3-24 07:04:25 | 只看該作者
17#
發(fā)表于 2025-3-24 14:27:36 | 只看該作者
18#
發(fā)表于 2025-3-24 17:12:30 | 只看該作者
19#
發(fā)表于 2025-3-24 21:26:05 | 只看該作者
20#
發(fā)表于 2025-3-24 23:58:29 | 只看該作者
A Time-Varying Inertia Weight Strategy for Particles Swarms Based on Self-Organized Criticalityxperimental setup compares the new algorithm with versions of the PSO with varying inertia weight, including a state-of-the-art algorithm with dynamic variation of the parameters. The results demonstrate that the BSt-PSO is clearly faster than the other algorithms in meeting the convergence criteria.
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