ConBreO: a music performance rendering system using hybrid approach of IEC and automated evolution

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

@InProceedings{Tanji:2010:gecco,
  author =       "Makoto Tanji and Hitoshi Iba",
  title =        "ConBreO: a music performance rendering system using
                 hybrid approach of IEC and automated evolution",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "1275--1282",
  keywords =     "genetic algorithms, genetic programming, Real world
                 applications",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830711",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper presents an IEC (Interactive Evolutionary
                 Computation) system named ConBreO to render expressive
                 music performance using Genetic Programming. The
                 central problem of IEC is the limitation of number of
                 fitness evaluations because of user fatigue. In the
                 system, we introduce two support techniques for IEC.
                 The first one is a hybrid approach of IEC and automated
                 evolution which allows the system to evolve both of IEC
                 and automated evolution. The second one is the
                 selective presentation which selects a new individual
                 to be evaluated by the user based on its expected
                 improvement of fitness. Using the system, obtained
                 expression rule won an award at a performance rendering
                 contest which evaluates computer systems generating
                 expressive musical performances. Our experiment shows
                 that the selective presentation reduces the number of
                 fitness evaluations required to construct the fitness
                 prediction model and prevents the system evaluating
                 unfruitful individuals.",
  notes =        "Also known as \cite{1830711} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",
}

Genetic Programming entries for Makoto Tanji Hitoshi Iba

Citations