Synthesizing Round Based Fault-Tolerant Programs Using Genetic Programming

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

@InProceedings{conf/sss/ZhuK13,
  author =       "Ling Zhu and Sandeep Kulkarni",
  title =        "Synthesizing Round Based Fault-Tolerant Programs Using
                 Genetic Programming",
  booktitle =    "Proceedings of the 15th International Symposium on
                 Stabilization, Safety, and Security of Distributed
                 Systems (SSS 2013)",
  year =         "2013",
  editor =       "Teruo Higashino and Yoshiaki Katayama and 
                 Toshimitsu Masuzawa and Maria Potop-Butucaru and 
                 Masafumi Yamashita",
  volume =       "8255",
  series =       "Lecture Notes in Computer Science",
  pages =        "370--372",
  address =      "Osaka, Japan",
  month =        nov # " 13-16",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 NSGA-II",
  bibdate =      "2013-11-11",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/sss/sss2013.html#ZhuK13",
  isbn13 =       "978-3-319-03088-3",
  URL =          "http://dx.doi.org/10.1007/978-3-319-03089-0",
  URL =          "http://dx.doi.org/10.1007/978-3-319-03089-0_33",
  DOI =          "doi:10.1007/978-3-319-03089-0_33",
  size =         "3 pages",
  abstract =     "In this paper, we present an approach to synthesise
                 round based distributed fault-tolerant programs using
                 stack based genetic programming. Our approach evolves a
                 fault-tolerant program based on a round based structure
                 and the program specification. To permit such
                 evolution, we use a multi-objective fitness function
                 that characterises the correctness of the program in
                 the absence of faults, in the presence of a single
                 fault and in the presence of multiple faults. This
                 multi-objective fitness function attempts to synthesise
                 a program that works equally well in all these
                 scenarios. We demonstrate the effectiveness of our
                 approach using two case studies: a byzantine agreement
                 problem and a token ring problem.",
  notes =        "Operator stack, values stack, cites
                 \cite{MSU-CSE-13-9}",
}

Genetic Programming entries for Ling Zhu Sandeep Kulkarni

Citations