夹克怕包裹 发表于 2025-3-26 23:41:58
Jochen Peter Breuer,Pierre Frotove the qualities of the generated programs, e.g., readability and performance. Here we focus on program search with grammatical evolution, which produces code that has different structure compared to human-generated code, e.g., loops and conditions are hardly used. We use a large code-corpus that w过份 发表于 2025-3-27 01:14:31
https://doi.org/10.1007/978-3-031-02056-8artificial intelligence; computer programming; computer systems; correlation analysis; distributed compuAtmosphere 发表于 2025-3-27 07:49:55
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Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence under a specific set of Bernoulli . distributions. A natural question is whether this mutation mechanism allows convergence under other distributions as well. Our experiments indicate that the answer to this question is affirmative and, at the very least, this mechanism converges under Bernoulli . distributions outside of the known proved regime.aquatic 发表于 2025-3-27 17:22:57
978-3-031-02055-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl松果 发表于 2025-3-27 17:47:08
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http://reply.papertrans.cn/39/3826/382574/382574_37.png尽管 发表于 2025-3-28 03:45:20
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One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Syy sensors and actuators of a CPS can be monitored for detecting cyber-attacks that introduce anomalies in those data. We use Signal Temporal Logic (STL) formulas to tightly describe the normal behavior of a CPS, identifying data instances that do not satisfy the formulas as anomalies. We learn an eninstructive 发表于 2025-3-28 12:56:51
Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Rnto unseen data. To address this issue, many pieces of research have been devoted to controlling the model complexity of GP. One recent work aims to control model complexity using a new representation called Adaptive Weighted Splines. With its semi-structured characteristic, the Adaptive Weighted Sp