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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Yu:2014:SEAL,

• 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 achieved.",

}

Genetic Programming entries for Yang Yu Hui Ma Mengjie Zhang

Citations