Evolving stochastic processes using feature tests and genetic programming

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

@InProceedings{DBLP:conf/gecco/RossI09,
  author =       "Brian J. Ross and Janine H. Imada",
  title =        "Evolving stochastic processes using feature tests and
                 genetic programming",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1059--1066",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570044",
  abstract =     "The synthesis of stochastic processes using genetic
                 programming is investigated. Stochastic process
                 behaviours take the form of time series data, in which
                 quantities of interest vary over time in a
                 probabilistic, and often noisy, manner. A suite of
                 statistical feature tests are performed on time series
                 plots from example processes, and the resulting feature
                 values are used as targets during evolutionary search.
                 A process algebra, the stochastic pi-calculus, is used
                 to denote processes. Investigations consider variations
                 of GP representations for a subset of the stochastic
                 pi-calculus, for example, the use of channel
                 unification, and various grammatical constraints.
                 Target processes of varying complexity are studied.
                 Results show that the use of grammatical GP with
                 statistical feature tests can successfully synthesize
                 stochastic processes. Success depends upon a selection
                 of appropriate feature tests for characterizing the
                 target behaviour, and the complexity of the target
                 process.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Brian J Ross Janine H Imada

Citations