A GP Approach to QoS-Aware Web Service Composition and Selection

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
                 and Selection",
  booktitle =    "Proceedings 10th International Conference on Simulated
                 Evolution and Learning, SEAL 2014",
  year =         "2014",
  editor =       "Grant Dick and Will N. Browne and Peter Whigham and 
                 Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and 
                 Yaochu Jin and Xiaodong Li and Yuhui Shi and 
                 Pramod Singh and Kay Chen Tan and Ke Tang",
  volume =       "8886",
  series =       "Lecture Notes in Computer Science",
  pages =        "180--191",
  address =      "Dunedin, New Zealand",
  month =        dec # " 15-18",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-13562-5",
  DOI =          "doi:10.1007/978-3-319-13563-2_16",
  abstract =     "Web services are independent functionality modules
                 that can be used as building blocks for applications
                 that accomplish more specific tasks. The large and
                 ever-growing number of Web services means that
                 performing this type of Web service composition
                 manually is infeasible, which leads to the exploration
                 of automated techniques to achieve this objective.
                 Evolutionary Computation (EC) approaches, in
                 particular, are a popular choice because they are
                 capable of efficiently handling the complex search
                 space involved in this problem. Therefore, we propose
                 the use of a Genetic Programming (GP) technique for Web
                 service composition, building upon previous work that
                 combines the identification of functionally correct
                 solutions with the consideration of the Quality of
                 Service (QoS) properties for each atomic service. The
                 proposed GP technique is compared with two PSO
                 composition techniques using the same QoS-aware
                 objective function, and results show that the solution
                 fitness values and execution times of the GP approach
                 are inferior to those of both PSO approaches, failing
                 to converge for larger datasets. This is because the
                 fitness function employed by the GP technique does not
                 have complete smoothness, thus leading to unreliable
                 behaviour during the evolution process. Multi-objective
                 GP and the use of functional correctness constraints
                 should be considered as alternatives to overcome this
                 in the future.",

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