Synthesizing Round Based Fault-Tolerant Programs using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Ling Zhu and Sandeep Kulkarni",
  title =        "Synthesizing Round Based Fault-Tolerant Programs using
                 Genetic Programming",
  number =       "MSU-CSE-13-9",
  institution =  "Department of Computer Science, Michigan State
  address =      "East Lansing, Michigan, USA",
  keywords =     "genetic algorithms, genetic programming, SBSE, Program
                 synthesis, Distributed programs, Fault-tolerance,
  month =        aug,
  year =         "2013",
  author1_email = "",
  author2_url =  "",
  author2_email = "",
  size =         "15",
  file =         "/user/web/htdocs/publications/tech/TR/",
  URL =          "",
  URL =          "",
  contact =      "",
  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 =        "Cited by \cite{conf/sss/ZhuK13}. PushGP operator stack
                 and values stack. Program structure (Fig 2) as nested
                 if(cond1)/elseif(condn) then write to variable fixed by
                 user before evolution. Ie GP works only on
                 cond1...condn. May be in future (sec 5) use model
                 checking to evaluate (assign fitness of) generated

Genetic Programming entries for Ling Zhu Sandeep Kulkarni