Using Multi-objective Genetic Programming to Synthesize Stochastic Processes

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{Ross:2009:GPTP,
  author =       "Brian J. Ross and Janine Imada",
  title =        "Using Multi-objective Genetic Programming to
                 Synthesize Stochastic Processes",
  booktitle =    "Genetic Programming Theory and Practice {VII}",
  year =         "2009",
  editor =       "Rick L. Riolo and Una-May O'Reilly and 
                 Trent McConaghy",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor",
  month =        "14-16 " # may,
  publisher =    "Springer",
  chapter =      "10",
  pages =        "159--175",
  keywords =     "genetic algorithms, genetic programming, stochastic
                 processes, process algebra, time-series feature tests,
                 multi-objective gp, MOGP",
  isbn13 =       "978-1-4419-1653-2",
  DOI =          "doi:10.1007/978-1-4419-1626-6_10",
  abstract =     "Genetic programming is used to automatically construct
                 stochastic processes written in the stochastic
                 pi-calculus. Grammar-guided genetic programming
                 constrains search to useful process algebra structures.
                 The time-series behaviour of a target process is
                 denoted with a suitable selection of statistical
                 feature tests. Feature tests can permit complex process
                 behaviours to be effectively evaluated. However, they
                 must be selected with care, in order to accurately
                 characterise the desired process behaviour.
                 Multi-objective evaluation is shown to be appropriate
                 for this application, since it permits heterogeneous
                 statistical feature tests to reside as independent
                 objectives. Multiple undominated solutions can be saved
                 and evaluated after a run, for determination of those
                 that are most appropriate. Since there can be a vast
                 number of candidate solutions, however, strategies for
                 filtering and analysing this set are required.",
  notes =        "part of \cite{Riolo:2009:GPTP}",
}

Genetic Programming entries for Brian J Ross Janine H Imada

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