A GP Approach to QoS-Aware Web Service Composition and Selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{daSilva:2014:SEAL,
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author = "Alexandre {Sawczuk da Silva} and Hui Ma and
Mengjie Zhang",
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title = "A {GP} Approach to {QoS}-Aware Web Service Composition
and Selection",
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booktitle = "Proceedings 10th International Conference on Simulated
Evolution and Learning, SEAL 2014",
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year = "2014",
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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",
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volume = "8886",
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series = "Lecture Notes in Computer Science",
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pages = "180--191",
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address = "Dunedin, New Zealand",
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month = dec # " 15-18",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-13562-5",
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DOI = "doi:10.1007/978-3-319-13563-2_16",
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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
Citations