Fragment-based Genetic Programming for Fully Automated Multi-objective Web Service Composition

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

@InProceedings{daSilva:2017:GECCO,
  author =       "Alexandre Sawczuk {da Silva} and Yi Mei and Hui Ma and 
                 Mengjie Zhang",
  title =        "Fragment-based Genetic Programming for Fully Automated
                 Multi-objective Web Service Composition",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "353--360",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071199",
  DOI =          "doi:10.1145/3071178.3071199",
  acmid =        "3071199",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, NSGA-II, QoS
                 optimisation, combinatorial optimisation,
                 multi-objective, representations, web service
                 composition",
  month =        "15-19 " # jul,
  abstract =     "Web services have become increasingly popular in
                 recent years, given their modular nature and
                 reusability potential. A particularly promising
                 application is in Web service composition, where
                 multiple individual services with specific
                 functionalities are composed to accomplish a more
                 complex task. Researchers have proposed evolutionary
                 computing techniques for creating compositions that are
                 not only feasible, but also have the best possible
                 Quality of Service (QoS). Some of these works employed
                 multi-objective techniques to tackle the optimisation
                 of compositions with conflicting QoS attributes, but
                 they are not fully automated, i.e. they assume the
                 composition work flow structure is already known. This
                 assumption is often not satisfied, as the workflow is
                 often unknown. This paper proposes a genetic
                 programming-based method to automatically generate
                 service compositions in a multi-objective context,
                 based on a novel fragmented tree representation. An
                 evaluation using benchmark datasets is carried out,
                 comparing existing methods adapted to the
                 multi-objective composition problem. Results show that
                 the fragmented method has the lowest execution time
                 overall. In terms of quality, its Pareto fronts are
                 equivalent to those of one of the approaches but
                 inferior to those of the other. More importantly, this
                 work provides a foundation for future investigation of
                 multi-objective fully automated service composition.",
  notes =        "Also known as \cite{daSilva:2017:FGP:3071178.3071199}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

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

Citations