Evolution of Stochastic Bio-Networks Using Summed Rank Strategies

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

@InProceedings{Ross:2011:EoSBUSRS,
  title =        "Evolution of Stochastic Bio-Networks Using Summed Rank
                 Strategies",
  author =       "Brian Ross",
  pages =        "772--779",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
                 Computation",
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Biometrics,
                 bioinformatics and biomedical applications",
  DOI =          "doi:10.1109/CEC.2011.5949697",
  abstract =     "Stochastic models defined in the stochastic
                 pi-calculus are evolved using genetic programming. The
                 interpretation of a stochastic model results in a set
                 of time series behaviours. Each time series denotes
                 changing quantities of components within the modelled
                 system. The time series are described by their
                 statistical features. This paper uses genetic
                 programming to reverse engineer stochastic pi-calculus
                 models. Given the statistical characteristics of the
                 intended model behavior, genetic programming attempts
                 to construct a model whose statistical features closely
                 match those of the target process. The feature
                 objectives comprising model behaviour are evaluated
                 using a multi-objective strategy. A contribution of
                 this research is that, rather than use conventional
                 Pareto ranking, a summed rank scoring strategy is used
                 instead. Summed rank scoring was originally derived for
                 high-dimensional search spaces. This paper shows that
                 it is likewise effective for evaluating stochastic
                 models with low- to moderate-sized search spaces. Two
                 models with oscillating behaviours were successfully
                 evolved, and these results are superior to those
                 obtained from earlier research attempts. Experiments on
                 a larger-sized model were not successful. Reasons for
                 its poor performance likely include inappropriate
                 choices in feature selection, and too many selected
                 features and channels contributing to an overly
                 difficult search space.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",
}

Genetic Programming entries for Brian J Ross

Citations