Using Model Checking Techniques For Evaluating the Effectiveness of Evolutionary Computing in Synthesis of Distributed Fault-Tolerant Programs

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

@InProceedings{Zhu:2015:GECCO,
  author =       "Ling Zhu and Sandeep Kulkarni",
  title =        "Using Model Checking Techniques For Evaluating the
                 Effectiveness of Evolutionary Computing in Synthesis of
                 Distributed Fault-Tolerant Programs",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1119--1126",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754779",
  DOI =          "doi:10.1145/2739480.2754779",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In most applications using genetic programming (GP),
                 objective functions are obtained by a terminating
                 calculation. However, the terminating calculation
                 cannot evaluate distributed fault-tolerant programs
                 accurately. A key distinction in synthesizing
                 distributed fault-tolerant programs is that they are
                 inherently non-deterministic, potentially having
                 infinite computations and executing in an unpredictable
                 environment. In this study, we apply a model checking
                 technique - Binary Decision Diagrams (BDDs) - to GP,
                 evaluating distributed programs by computing reachable
                 states of the given program and identifying whether it
                 satisfies its specification. We present scenario-based
                 multi-objective approach that each program is evaluated
                 under different scenarios which represent various
                 environments. The computation of the programs are
                 considered in two different semantics respectively:
                 interleaving and maximum-parallelism. In the end, we
                 illustrate our approach with a Byzantine agreement
                 problem, a token ring problem and a consensus protocol
                 using failure detector S. For the first time, this work
                 automatically synthesizes the consensus protocol with
                 S. The results show the proposed method enhances the
                 effectiveness of GP in all studied cases when using
                 maximum-parallelism semantic.",
  notes =        "Also known as \cite{2754779} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",
}

Genetic Programming entries for Ling Zhu Sandeep Kulkarni

Citations