Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Pilat:2016:ICTAI,
  author =       "Martin Pilat and Tomas Kren and Roman Neruda",
  booktitle =    "2016 IEEE 28th International Conference on Tools with
                 Artificial Intelligence (ICTAI)",
  title =        "Asynchronous Evolution of Data Mining Workflow Schemes
                 by Strongly Typed Genetic Programming",
  year =         "2016",
  pages =        "577--584",
  abstract =     "This paper describes an algorithm for the automated
                 design of whole machine learning work-flows, including
                 preprocessing of the data and automatic creation of
                 several types of ensembles. The algorithm is based on
                 strongly typed genetic programming which ensures the
                 validity of the workflows. The evolution of the
                 individuals in the population is asynchronous in order
                 to improve the usage of computational resources. The
                 approach is validated on four data sets from the UCI
                 machine learning repository.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICTAI.2016.0094",
  month =        nov,
  notes =        "Also known as \cite{7814654}",
}

Genetic Programming entries for Martin Pilat Tomas Kren Roman Neruda

Citations