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Titlebook: Genetic Programming Theory and Practice IV; Rick Riolo,Terence Soule,Bill Worzel Book 2007 Springer-Verlag US 2007 Automat.Boosting.algori

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51#
發(fā)表于 2025-3-30 09:17:25 | 只看該作者
https://doi.org/10.1007/978-0-387-49650-4Automat; Boosting; algorithm; algorithms; artificial intelligence; classification; complex system; genetic
52#
發(fā)表于 2025-3-30 13:48:23 | 只看該作者
Genome-Wide Genetic Analysis Using Genetic Programming: The Critical Need for Expert Knowledge,eliefF (TuRF) in a multi-objective fitness function that also includes accuracy significantly improves the performance of GP over that of random search. This study demonstrates that GP may be a useful computational discovery tool in this domain. This study raises important questions about the genera
53#
發(fā)表于 2025-3-30 19:15:42 | 只看該作者
54#
發(fā)表于 2025-3-31 00:45:45 | 只看該作者
55#
發(fā)表于 2025-3-31 02:21:49 | 只看該作者
Applying Genetic Programming to Reservoir History Matching Problem,the forecasts derived from a small number of computer simulation runs..We have applied the proposed technique to a West African oil field that has complex geology. The results show that GP is able to deliver high quality proxies. Meanwhile, important information about the reservoirs was revealed fro
56#
發(fā)表于 2025-3-31 06:26:45 | 只看該作者
Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphond graphs specifying component topology and parameter values for an example task, designing a passive analog low-pass filter with fifth-order Bessel characteristics. It explores three alternative design approaches. The first uses “standard” GP and evolves designs in which components can take on arb
57#
發(fā)表于 2025-3-31 09:19:51 | 只看該作者
58#
發(fā)表于 2025-3-31 14:30:35 | 只看該作者
Book 2007research, to an engineering methodology applied to commercial applications. It is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence..
59#
發(fā)表于 2025-3-31 18:50:32 | 只看該作者
Structure and Technical RequirementseliefF (TuRF) in a multi-objective fitness function that also includes accuracy significantly improves the performance of GP over that of random search. This study demonstrates that GP may be a useful computational discovery tool in this domain. This study raises important questions about the genera
60#
發(fā)表于 2025-3-31 23:30:07 | 只看該作者
Peter Mertens,Hans Wilhelm Wieczorreke number of cascades, the number of generations, and the population size. The optimal values for the three parameters have been defined based on second order regression models with .. hig herthan 0.97 for small, medium, and large-sized data sets. The robustness of the optimal parameters toward the t
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