Genetic programming for QoS-aware web service composition and selection

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{journals/soco/SilvaMZ16,
  author =       "Alexandre Sawczuk {da Silva} and Hui Ma and 
                 Mengjie Zhang",
  title =        "Genetic programming for {QoS-aware} web service
                 composition and selection",
  journal =      "Soft Computing",
  year =         "2016",
  number =       "10",
  volume =       "20",
  pages =        "3851--3867",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, conditional
                 constraint",
  bibdate =      "2017-05-20",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.1007/s00500-016-2096-z;
                 DBLP,
                 http://dblp.uni-trier.de/db/journals/soco/soco20.html#SilvaMZ16",
  DOI =          "doi:10.1007/s00500-016-2096-z",
  abstract =     "Web services, which can be described as functionality
                 modules invoked over a network as part of a larger
                 application are often used in software development.
                 Instead of occasionally incorporating some of these
                 services in an application, they can be thought of as
                 fundamental building blocks that are combined in a
                 process known as Web service composition. Manually
                 creating compositions from a large number of candidate
                 services is very time consuming, and developing
                 techniques for achieving this objective in an automated
                 manner becomes an active research field. One promising
                 group of techniques encompasses evolutionary computing,
                 which can effectively tackle the large search spaces
                 characteristic of the composition problem. Therefore,
                 this paper proposes the use of genetic programming for
                 Web service composition, investigating three variations
                 to ensure the creation of functionally correct
                 solutions that are also optimised according to their
                 quality of service. A variety of comparisons are
                 carried out between these variations and two particle
                 swarm optimisation approaches, with results showing
                 that there is likely a trade-off between execution time
                 and the quality of solutions when employing genetic
                 programming and particle swarm optimisation. Even
                 though genetic programming has a higher execution time
                 for most datasets, the results indicate that it scales
                 better than particle swarm optimisation.",
}

Genetic Programming entries for Alexandre Sawczuk da Silva Hui Ma Mengjie Zhang

Citations