Applications of Model Reuse When Using Estimation of Distribution Algorithms to Test Concurrent Software

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

@InProceedings{Staunton:2011:SSBSE,
  author =       "Jan Staunton and John A. Clark",
  title =        "Applications of Model Reuse When Using Estimation of
                 Distribution Algorithms to Test Concurrent Software",
  year =         "2011",
  booktitle =    "Search Based Software Engineering",
  editor =       "Myra Cohen and Mel O'Cinneid",
  volume =       "6956",
  series =       "Lecture Notes in Computer Science",
  pages =        "97--111",
  address =      "Szeged, Hungary",
  month =        "10-12 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, EDA, SBSE",
  isbn13 =       "978-3-642-23715-7",
  DOI =          "doi:10.1007/978-3-642-23716-4_12",
  abstract =     "Previous work has shown the efficacy of using
                 Estimation of Distribution Algorithms (EDAs) to detect
                 faults in concurrent software/systems. A promising
                 feature of EDAs is the ability to analyse the
                 information or model learnt from any particular
                 execution. The analysis performed can yield insights
                 into the target problem allowing practitioners to
                 adjust parameters of the algorithm or indeed the
                 algorithm itself. This can lead to a saving in the
                 effort required to perform future executions, which is
                 particularly important when targeting expensive fitness
                 functions such as searching concurrent software state
                 spaces. In this work, we describe practical scenarios
                 related to detecting concurrent faults in which reusing
                 information discovered in EDA runs can save effort in
                 future runs, and prove the potential of such reuse
                 using an example scenario. The example scenario
                 consists of examining problem families, and we provide
                 empirical evidence showing real effort saving
                 properties for three such families.",
  affiliation =  "Department of Computer Science, University of York,
                 UK",
  notes =        "Uses \cite{poli08:_linear_estim_distr_gp_system}
                 PROMELA",
}

Genetic Programming entries for Jan Staunton John A Clark

Citations