Using Genetic Programming to Identify Tradeoffs in Self-Stabilizing Programs: A Case Study

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

@InProceedings{Zhu:2015:ieeeICDCSW,
  author =       "Ling Zhu and Sandeep S. Kulkarni",
  booktitle =    "35th IEEE International Conference on Distributed
                 Computing Systems Workshops",
  title =        "Using Genetic Programming to Identify Tradeoffs in
                 Self-Stabilizing Programs: A Case Study",
  year =         "2015",
  pages =        "29--34",
  abstract =     "We focus on the use of genetic programming to identify
                 trade-offs between closure and convergence properties
                 of a stabilizing program. Closure property
                 characterises the behaviour in the absence of faults
                 whereas convergence property characterises the recovery
                 from an arbitrary state to a legitimate state. We
                 describe how genetic programming (GP) can be applied to
                 identify a trade-off for the behaviours in the absence
                 of faults and in the presence of faults. This approach
                 uses BDD (Binary Decision Diagram) based techniques.
                 Subsequently, we use two objectives: closure and
                 maximum convergence time and use NSGA-II (a
                 multi-objective optimisation algorithm) to identify the
                 trade-off between the performances. We use the classic
                 K-state token ring program to illustrate the trade-off
                 and run experiments for three different approaches: (1)
                 where we only consider trade-off based on process 0,
                 (2) where only consider trade-off based on non-zero
                 processes, and (3) where we consider both trade-offs.
                 Several interesting results are found such as, special
                 process (marked N - 3 in the K-state program) plays a
                 critical role in providing the trade-off, while process
                 1 is the least important.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICDCSW.2015.17",
  ISSN =         "1545-0678",
  month =        jun,
  notes =        "Dept. of Comput. Sci. & Eng., Michigan State Univ.,
                 East Lansing, MI, USA

                 Also known as \cite{7165080}",
}

Genetic Programming entries for Ling Zhu Sandeep Kulkarni

Citations