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Titlebook: Genetic Programming Theory and Practice VIII; Rick Riolo,Trent McConaghy,Ekaterina Vladislavleva Book 2011 Springer Science+Business Media

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發(fā)表于 2025-3-23 11:28:48 | 只看該作者
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發(fā)表于 2025-3-23 14:29:18 | 只看該作者
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發(fā)表于 2025-3-23 21:44:55 | 只看該作者
https://doi.org/10.1007/978-1-4419-7747-2Genetic Programming; Genetic Programming Applications; Genetic Programming Theory; Symbolic regression;
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發(fā)表于 2025-3-23 22:42:33 | 只看該作者
Rick Riolo,Trent McConaghy,Ekaterina VladislavlevaPresents large-scale, real-world applications of GP.Addresses a variety of problem domains that respond to GP solutions.Written by leading researchers and practitioners in the field.Includes supplemen
15#
發(fā)表于 2025-3-24 06:01:16 | 只看該作者
,Prüfung statistischer Hypothesen (Tests),h evolution of expressions using these definitions takes place. We have recently developed a system, dubbed FINCH (.ertile Darw.ian Byte.ode .arvester), to evolutionarily improve actual, . software, which was . for the purpose of serving as a GP representation in particular, nor for evolution in gen
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發(fā)表于 2025-3-24 08:50:58 | 只看該作者
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發(fā)表于 2025-3-24 12:09:20 | 只看該作者
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發(fā)表于 2025-3-24 16:40:54 | 只看該作者
https://doi.org/10.1007/978-3-531-92042-9requires independent training cases and can only use voting as a cooperation mechanism. This paper compares AdaBoost to Orthogonal Evolution of Teams (OET), an approach for generating ensembles that allows for a much wider range of problems and cooperation mechanisms. The set of test problems includ
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發(fā)表于 2025-3-24 21:34:51 | 只看該作者
Das Aufbereiten von Textausgaben in such a way to guarantee that the mean program size will either keep a particular value (e.g., its initial value) or will follow a schedule chosen by the user. The mathematical derivation of the technique as well as its numerical and empirical corroboration are presented.
20#
發(fā)表于 2025-3-25 01:43:45 | 只看該作者
Das Aufbereiten von Textausgabenon to the usual computational functions found in CGP, SMCGP includes functions that can modify the evolved program at run time. This means that programs can be iterated to produce an infinite sequence of phenotypes from a single evolved genotype. Here, we discuss the results of using SMCGP on a vari
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