古董
发表于 2025-3-25 05:57:35
Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and DataSR), which represents the models as short interpretable mathematical formulas. The integration of knowledge into symbolic regression via shape constraints is discussed alongside three real-world applications: modeling magnetization curves, modeling twin-screw extruders and model-based data validation.
instill
发表于 2025-3-25 08:01:50
Stephan Winkler,Leonardo Trujillo,Ting HuExplores the intersection of GP and evolutionary computation, with machine learning and deep learning methods.Provides a unique combination of theoretical contributions and state-of-the-art real-world
不容置疑
发表于 2025-3-25 15:40:39
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600
发表于 2025-3-25 16:30:36
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亲爱
发表于 2025-3-25 22:39:10
https://doi.org/10.1007/978-981-99-8413-8Genetic Programming; Genetic Programming Applications; Model Discovery; Ethics in Computer Science; Symb
OVER
发表于 2025-3-26 03:34:30
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larder
发表于 2025-3-26 04:57:18
Genetic Programming Theory and Practice XX978-981-99-8413-8Series ISSN 1932-0167 Series E-ISSN 1932-0175
暂时休息
发表于 2025-3-26 12:21:08
https://doi.org/10.1007/978-3-030-73924-9as rebuilt from the ground up to be more modular, easier to maintain, and easier to expand. TPOT2 comes with new features and optimizations, such as a more flexible graph-based representation of Scikit-Learn pipelines and the ability to specify various aspects of the evolutionary run. Using experime
Ruptured-Disk
发表于 2025-3-26 16:03:39
South of the Northeast Kingdom,al topology. To achieve more clarity in how a spatial topology impacts performance and complexity we introduce a spatial topology to a pairwise dominance coevolutionary algorithm named PDCoEA. The new algorithm is called STPDCoEA. We use a methodology for consistent algorithm comparison to empirical
MUT
发表于 2025-3-26 18:30:57
https://doi.org/10.1057/9781137305190 systems, decision tree genetic programming and SEE-Segment. Active learning was shown to improve the rate and consistency at which good models are found while reducing the required number of training samples to achieve good solutions in both ML systems. The importance of diversity in ensembles for