Automated Web Service Composition Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Liyuan Xiao and Carl K. Chang and Hen-I Yang and 
                 Kai-Shin Lu and Hsin-yi Jiang",
  booktitle =    "36th Annual IEEE Computer Software and Applications
                 Conference Workshops (COMPSACW 2012)",
  title =        "Automated Web Service Composition Using Genetic
  year =         "2012",
  pages =        "7--12",
  month =        "16-20 " # jul,
  address =      "Izmir",
  keywords =     "genetic algorithms, genetic programming, Web services,
                 convergence, graph theory, knowledge based systems,
                 probability, program testing, semantic networks, SDG,
                 atomic Web service, automated Web service composition,
                 black-box testing, business integration, convergence
                 process, convergence rate, input analysis, knowledge
                 rules, mutation operation, output analysis, population
                 quality, probability, semantic meaning, service
                 dependency graph, Complexity theory, Semantics,
                 Sociology, Statistics, Syntactics, Testing, Web
                 services, black-box testing, functional requirements,
                 services composition, test cases",
  isbn13 =       "978-1-4673-2714-5",
  DOI =          "doi:10.1109/COMPSACW.2012.12",
  size =         "6 pages",
  abstract =     "Automated web service composition can largely reduce
                 human efforts in business integration. We present an
                 approach to fully automate web service composition
                 without workflow or knowing the semantic meaning of
                 atomic web service. The experiment results show that
                 the accuracy of our composition method using Genetic
                 Programming (GP), in terms of the number of times an
                 expected composition that can be derived versus the
                 total number of runs, can be over 90percent. Based on
                 the traditional GP used in web service composition, our
                 algorithm achieved improvements in three aspects: 1. We
                 do black-box testing on each individual in each
                 population. The success rate of tests is taken into
                 account by the fitness function of GP so that the
                 convergence rate can be faster; 2. We comply with
                 services knowledge rules such as service dependency
                 graph (SDG) when generating individual web service
                 compositions in each population to improve the
                 convergence process and population quality; 3. We
                 choose cross-over or mutation operation based on the
                 parent individuals' input and output analysis instead
                 of by probability as typically done in related work. In
                 this way, GP can generate better children even under
                 the same parents.",
  notes =        "Also known as \cite{6341542}",

Genetic Programming entries for Liyuan Xiao Carl K Chang Hen-I Yang Kai-Shin Lu Hsin-yi Jiang