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Titlebook: Genetic Programming; 26th European Confer Gisele Pappa,Mario Giacobini,Zdenek Vasicek Conference proceedings 2023 The Editor(s) (if applica

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樓主: purulent
11#
發(fā)表于 2025-3-23 10:42:16 | 只看該作者
12#
發(fā)表于 2025-3-23 14:19:41 | 只看該作者
Using FPGA Devices to?Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with?Receces, which is motivated by the fact that FPGAs can sometimes leverage larger amounts of data/function parallelism, as well as better energy efficiency, when compared to general-purpose CPU/GPU systems. In our preliminary study, we introduce a fixed-depth, tree-based architecture capable of evaluatin
13#
發(fā)表于 2025-3-23 21:20:43 | 只看該作者
Memetic Semantic Genetic Programming for?Symbolic Regressioninto a population-based process, semantic-based algorithms allow one to improve them locally to achieve a desired output. Hence, combining memetic and semantic enables us to (a) enhance the exploration and exploitation features of?genetic programming (GP) and (b) discover short symbolic expressions
14#
發(fā)表于 2025-3-24 01:59:13 | 只看該作者
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發(fā)表于 2025-3-24 07:28:08 | 只看該作者
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發(fā)表于 2025-3-24 10:41:02 | 只看該作者
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發(fā)表于 2025-3-24 16:32:58 | 只看該作者
https://doi.org/10.1007/978-3-476-04267-5he proposed method, the experimental phase compares it against well-known diversity maintenance methods over well-known benchmarks. Experimental results clearly demonstrate the suitability of the proposed self-adaptive approach and the possibility of applying it to different types of crossover and EAs.
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發(fā)表于 2025-3-24 21:57:55 | 只看該作者
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發(fā)表于 2025-3-25 01:08:57 | 只看該作者
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