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https://doi.org/10.1007/978-981-16-8113-4Genetic Programming; Genetic Programming Theory; Genetic Programming Applications; Symbolic Regression;明确 发表于 2025-3-23 20:00:26
Wolfgang Banzhaf,Leonardo Trujillo,Bill WorzelProvides papers describing cutting-edge work on the theory and applications of genetic programming (GP).Offers large-scale, real-world applications (big data) of GP to a variety of problem domains.Pre接触 发表于 2025-3-24 00:23:23
https://doi.org/10.1057/9780230372412grams into graphs of teams of programs. To date, the framework has been demonstrated on reinforcement learning tasks with stochastic partially observable state spaces or time series prediction. However, evolving solutions to reinforcement tasks often requires agents to demonstrate/ juggle multiple psingle 发表于 2025-3-24 02:23:21
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https://doi.org/10.1007/978-3-030-45537-8sured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly pre排斥 发表于 2025-3-24 18:14:52
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,: A Precarious Restoration to “the Real”,st small data sets to reward model generalization. ESSENCE (2009) [.] extended the OrdinalGP concept to handle imbalanced data by using the SMITS algorithm to rank data records according to their information content to avoid locking into the behavior of heavily sampled data regions but had the disad