A Hybrid GP-Tabu Approach to QoS-Aware Data Intensive Web Service Composition

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

  author =       "Yang Yu and Hui Ma and Mengjie Zhang",
  title =        "A Hybrid GP-Tabu Approach to QoS-Aware Data Intensive
                 Web Service Composition",
  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 =        "106--118",
  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_10",
  abstract =     "Web service composition has become a promising
                 technique to build powerful business applications by
                 making use of distributed services with different
                 functions. Due to the explosion in the volume of data,
                 providing efficient approaches to composing data
                 intensive services will become more and more important
                 in the field of service-oriented computing. Meanwhile,
                 as numerous web services have been emerging to offer
                 identical or similar functionality, web service
                 composition is usually performed with end-to-end
                 Quality of Service (QoS) properties which are adopted
                 to describe the non-functional properties (e.g.,
                 response time, execution cost, reliability, etc.) of a
                 web service. In this paper, a hybrid approach that
                 integrates the use of genetic programming and tabu
                 search to QoS-aware data intensive service composition
                 is proposed. The performance of the proposed approach
                 is evaluated using the publicly available benchmark
                 datasets. A full set of experimental results show that
                 a significant improvement of our approach over that
                 obtained by the simple genetic programming method and
                 several traditional optimization methods, has been

Genetic Programming entries for Yang Yu Hui Ma Mengjie Zhang