P-Tree programming
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- @InProceedings{Oesch:2017:ieeeSSCI,
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author = "Christian Oesch",
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booktitle = "2017 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "P-Tree programming",
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year = "2017",
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abstract = "We propose a novel method for automatic program
synthesis. P-Tree Programming represents the program
search space through a single probabilistic prototype
tree. From this prototype tree we form program
instances which we evaluate on a given problem. The
error values from the evaluations are propagated
through the prototype tree. We use them to update the
probability distributions that determine the symbol
choices of further instances. The iterative method is
applied to several symbolic regression benchmarks from
the literature. It outperforms standard Genetic
Programming to a large extent. Furthermore, it relies
on a concise set of parameters which are held constant
for all problems. The algorithm can be employed for
most of the typical computational intelligence tasks
such as classification, automatic program induction,
and symbolic regression.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI.2017.8280849",
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month = nov,
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notes = "Also known as \cite{8280849}",
- }
Genetic Programming entries for
Christian Oesch
Citations