Evolving Workflow Graphs Using Typed Genetic Programming

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@InProceedings{Ken:2015:ieeeSSCI,
  author =       "Tomas Kren and Martin Pilat and Roman Neruda",
  booktitle =    "2015 IEEE Symposium Series on Computational
                 Intelligence",
  title =        "Evolving Workflow Graphs Using Typed Genetic
                 Programming",
  year =         "2015",
  pages =        "1407--1414",
  abstract =     "When applying machine learning techniques to more
                 complicated datasets, it is often beneficial to use
                 ensembles of simpler models instead of a single, more
                 complicated, model. However, the creation of ensembles
                 is a tedious task which requires a lot of human
                 interaction and experimentation. In this paper, we
                 present a technique for construction of ensembles based
                 on typed genetic programming. The technique describes
                 an ensemble as a directed acyclic graph, which is
                 internally represented as a tree evolved by the genetic
                 programming. The approach is evaluated in a series of
                 experiments on various datasets and compared to the
                 performance of simple models tuned by grid search, as
                 well as to ensembles generated in a systematic
                 manner.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2015.200",
  month =        dec,
  notes =        "Fac. of Math. & Phys., Charles Univ. in Prague,
                 Prague, Czech Republic

                 Also known as \cite{7376776}",
}

Genetic Programming entries for Tomas Kren Martin Pilat Roman Neruda

Citations