Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems

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

@Article{Zhang:2009:ieeetASE,
  author =       "Yang Zhang and Peter I. Rockett",
  title =        "Application of Multiobjective Genetic Programming to
                 the Design of Robot Failure Recognition Systems",
  journal =      "IEEE Transactions on Automation Science and
                 Engineering",
  year =         "2009",
  month =        apr,
  volume =       "6",
  number =       "2",
  pages =        "372--376",
  keywords =     "genetic algorithms, genetic programming, classifiers,
                 data-driven machine learning method, domain knowledge,
                 domain-dependent feature extraction, multiobjective
                 genetic programming, robot failure recognition systems,
                 control engineering computing, feature extraction,
                 learning (artificial intelligence), telerobotics",
  abstract =     "We present an evolutionary approach using
                 multiobjective genetic programming (MOGP) to derive
                 optimal feature extraction preprocessing stages for
                 robot failure detection. This data-driven machine
                 learning method is compared both with conventional
                 (nonevolutionary) classifiers and a set of
                 domain-dependent feature extraction methods. We
                 conclude MOGP is an effective and practical design
                 method for failure recognition systems with enhanced
                 recognition accuracy over conventional classifiers,
                 independent of domain knowledge.",
  DOI =          "doi:10.1109/TASE.2008.2004414",
  ISSN =         "1545-5955",
  notes =        "Also known as \cite{4667633}",
}

Genetic Programming entries for Yang Zhang Peter I Rockett

Citations