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Titlebook: Genetic Programming; 6th European Confere Conor Ryan,Terence Soule,Ernesto Costa Conference proceedings 2003 Springer-Verlag Berlin Heidelb

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樓主: 天真無邪
51#
發(fā)表于 2025-3-30 11:21:39 | 只看該作者
52#
發(fā)表于 2025-3-30 16:25:55 | 只看該作者
53#
發(fā)表于 2025-3-30 19:23:52 | 只看該作者
Evolutionary Design of Objects Using Scene Graphs chosen at random, then remain fixed during the run. We also experimented with an evolution strategy to adapt the internal variables. Experimental results are presented for the evolution of a wind turbine. The VRML representation produced better results.
54#
發(fā)表于 2025-3-30 21:03:00 | 只看該作者
55#
發(fā)表于 2025-3-31 01:03:31 | 只看該作者
Gesellschaft und sozialer Wandel,ists. One of the things needed for this study is a tool that can create microstructural patterns. In this paper we explore the use of a genetic algorithm to evolve the rules of an effector automata to recreate these microstructural patterns.
56#
發(fā)表于 2025-3-31 07:14:38 | 只看該作者
https://doi.org/10.1007/978-3-531-90659-1id rule sets that are easy to interpret. The GP is compared with C4.5 in a real-world medical data set. This data set represents a difficult classification problem, and a new preprocessing method was devised for mining the data.
57#
發(fā)表于 2025-3-31 10:16:15 | 只看該作者
58#
發(fā)表于 2025-3-31 17:05:09 | 只看該作者
https://doi.org/10.1007/978-3-658-05291-1iminary results on a large data set show that the ensemble of classifiers trained on a sample of the data obtains higher accuracy than a single classifier that uses the entire data set at a much lower computational cost.
59#
發(fā)表于 2025-3-31 18:42:42 | 只看該作者
https://doi.org/10.1007/978-3-322-98418-0r, thanks to evolved profiling information. Tests on the “Cystic Fibrosis Database” benchmark [.] show that, while suggesting original documents by adaptation of its internal rules to the context of the user, ELISE is able to improve its recall rate.
60#
發(fā)表于 2025-4-1 01:07:53 | 只看該作者
Christoph Haferburg,Armin Osmanovichd how systematic estimation errors are introduced by a low number of experiments. Two reasons examined are the number of unsuccessful experiments and the variation in the number of fitness evaluations necessary to find a solution among the successful experiments.
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