Using Genetic Programming and Decision Trees for Generating Structural Descriptions of Four Bar Mechanisms

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@Article{ekart:2003:AIEDAM,
  author =       "Aniko Ekart and Andras Markus",
  title =        "Using Genetic Programming and Decision Trees for
                 Generating Structural Descriptions of Four Bar
                 Mechanisms",
  journal =      "Artificial Intelligence for Engineering Design,
                 Analysis and Manufacturing",
  year =         "2003",
  volume =       "17",
  number =       "3",
  pages =        "205--220",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, decision
                 trees, four bar mechanism synthesis, machine learning",
  ISSN =         "0890-0604",
  DOI =          "doi:10.1017/S0890060403173039",
  abstract =     "Four bar mechanisms are basic components of many
                 important mechanical device. The kinematic synthesis of
                 four bar mechanisms is a difficult design problem.

                 We present here a novel method that combines the
                 genetic programming and decision tree learning
                 methods.

                 We give a structural description for the class of
                 mechanisms that produce desired coupler curves. For
                 finding and characterising feasible regions of the
                 design space constructive induction is used. Decision
                 trees constitute the learning engine and the new
                 features are created by genetic programming.",
  notes =        "http://journals.cambridge.org/action/displayJournal?jid=AIE",
}

Genetic Programming entries for Aniko Ekart Andras Markus

Citations