A GP Approach to QoS-Aware Web Service Composition including Conditional Constraints

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Alexandre Sawczuk {da Silva} and Hui Ma and 
                 Mengjie Zhang",
  title =        "A GP Approach to {QoS}-Aware Web Service Composition
                 including Conditional Constraints",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "2113--2120",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257145",
  abstract =     "Automated Web service composition is one of the holy
                 grails of service-oriented computing, since it allows
                 users to create an application simply by specifying the
                 inputs the resulting application should require, the
                 outputs it should produce, and any constraints it
                 should respect. The composition problem has been
                 handled using a variety of techniques, from AI planning
                 to optimisation algorithms, however no approach so far
                 has focused on handling three composition dimensions
                 simultaneously, producing solutions that are: (1) fully
                 functional (i.e. fully executable), (2) respect
                 conditional constraints (e.g. user can specify logical
                 branching), and (3) are optimised according to
                 nonfunctional Quality of Service (QoS) measurements.
                 This paper presents a genetic programming approach that
                 addresses these three dimensions simultaneously through
                 the fitness function, as well as through the
                 enforcement of constraints to candidate trees during
                 initialisation, mutation, and crossover. The approach
                 is tested using an extended version of the WSC2008
                 datasets, and results show that fully functional and
                 quality-optimised solutions can be created for all
                 associated tasks, with an execution time that is
                 roughly equivalent to that of a non-conditional
  notes =        "1415 hrs 15070 CEC2015",

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