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Titlebook: Artificial Intelligence and Soft Computing; 12th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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發(fā)表于 2025-3-28 17:40:44 | 只看該作者
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發(fā)表于 2025-3-28 19:19:42 | 只看該作者
Die Berechnung des Gegenstandswertestion and a generational selection. We propose specialized genetic operators to mutate and cross-over individuals (trees). The fitness function is based on the Bayesian Information Criterion. In preliminary experimental evaluation we show the impact of the tree representation on solving different prediction problems.
43#
發(fā)表于 2025-3-29 00:27:47 | 只看該作者
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發(fā)表于 2025-3-29 05:36:13 | 只看該作者
Die Regelgebühren in gerichtlichen Verfahren function value. This paper examines several sea collision scenarios at different levels of difficulty. Based on those, the method has been tested to choose values of parameters, which significantly influence its effectiveness.
45#
發(fā)表于 2025-3-29 09:19:36 | 只看該作者
Aufbau und Aufgaben der Gerichtsbarkeindividually for each user during training and verification process. In this paper we propose a new approach to automatic evolutionary selection of the dynamic signature global features. Our method was tested with use of the SVC2004 public on-line signature database.
46#
發(fā)表于 2025-3-29 12:16:38 | 只看該作者
Die Gesch?ftsgebühr gem?? Nr. 2300 VV RVGed improvements with regards to the overall duration of the optimization. Our main aim is to provide practitioners of MOEAs with a simple but effective method of deciding which master-slave parallelization option is better when dealing with a time-constrained optimization process.
47#
發(fā)表于 2025-3-29 16:08:47 | 只看該作者
Die Gesch?ftsgebühr gem?? Nr. 2300 VV RVGthen their inclusion in the classical methods of exploratory data analysis is being discussed. Finally, some illustrative examples of presented approach in the tasks of cluster analysis and classification are being given.
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發(fā)表于 2025-3-29 22:43:13 | 只看該作者
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發(fā)表于 2025-3-30 02:10:37 | 只看該作者
50#
發(fā)表于 2025-3-30 06:40:32 | 只看該作者
https://doi.org/10.1007/978-3-642-38610-7bio-inspired techniques; genetic algorithms; interacting agents; particle swarm optimization; visualizat
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