Highly Accurate Symbolic Regression with Noisy Training Data

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@InProceedings{Korns:2015:GPTP,

• author = "Michael Korns",
• title = "Highly Accurate Symbolic Regression with Noisy Training Data",
• booktitle = "Genetic Programming Theory and Practice XIII",
• year = "2015",
• editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon",
• series = "Genetic and Evolutionary Computation",
• pages = "91--115",
• address = "Ann Arbor, USA",
• month = "14-16 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Abstract expression grammars, Grammar template genetic programming, Particle swarm",
• isbn13 = "978-3-319-34223-8",
• URL = "http://www.springer.com/us/book/9783319342214",
• DOI = "doi:10.1007/978-3-319-34223-8_6",
• abstract = "As symbolic regression (SR) has advanced into the early stages of commercial exploitation, the poor accuracy of SR, still plaguing even the most advanced commercial packages, has become an issue for early adopters. Users expect to have the correct formula returned, especially in cases with zero noise and only one basis function with minimally complex grammar depth...",
• notes = "

  Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016",

}

Genetic Programming entries for Michael Korns

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