EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 173 (2018) MATEC Web Conf., 173 (2018) 03051 References
Open Access
Issue
MATEC Web Conf.
Volume 173, 2018
2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
Article Number 03051
Number of page(s) 5
Section Digital Signal and Image Processing
DOI https://doi.org/10.1051/matecconf/201817303051
Published online 19 June 2018
  1. Liu, H., Zhong, F., Ouyang, B., & Wu, J. (2011). An Approach for QoS-Aware Web Service Composition Based on Improved Genetic Algorithm[C] International Conference on Web Information Systems and Mining. IEEE, 2011:123-128. [Google Scholar]
  2. Maulik U, Bandyopadhyay S. Genetic algorithm-based clustering technique[J]. Pattern Recognition, 2004, 33(9):1455-1465. [CrossRef] [Google Scholar]
  3. Zeb A, Khan M, Khan N, et al. Hybridization of simulated annealing with genetic algorithm for cell formation problem[J]. International Journal of Advanced Manufacturing Technology, 2016, 86(5-8):1-12. [CrossRef] [Google Scholar]
  4. Qian X, Huang M, Gao T, et al. An improved ant colony algorithm for winner determination in multi-attribute combinatorial reverse auction[C] Evolutionary Computation. IEEE, 2014:1917-1921. [Google Scholar]
  5. Duan H B, Wang D B, Zhu J Q, et al. Development on ant colony algorithm theory and its application[J]. Control & Decision, 2004, 19(12):1321-1320. [Google Scholar]
  6. Dong Y Y, Hong N I, Deng H J, et al. Service Selection Strategy Offering Global Optimal Qulity of Service[J]. Journal of Chinese Computer Systems, 2011, 32(3):455-459. [Google Scholar]
  7. Alrifai M, Skoutas D, Risse T. Selecting skyline services for QoS-based web service composition[C] International Conference on World Wide Web. ACM, 2010:11-20. [Google Scholar]
  8. Mishra B S P, Dehuri S, Mall R, et al. Parallel Single and Multiple Objectives Genetic Algorithms: A Survey[J]. International Journal of Applied Evolutionary Computation, 2011, 2(2):21-57. [CrossRef] [Google Scholar]
  9. Yun Y, Nakayama H. Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms[J]. Journal of Global Optimization, 2013, 57(2):367-384. [CrossRef] [Google Scholar]
  10. Xue C, Dong L, Li G. An Improved Immune Genetic Algorithm for the Optimization of Enterprise Information System based on Time Property[J]. Journal of Software, 2011, 6(3):436-443. [Google Scholar]
  11. Canfora G, Penta M D, Esposito R, et al. An approach for QoS-aware service composition based on genetic algorithms[C] Conference on Genetic and Evolutionary Computation. 2005:1069-1075. [Google Scholar]
  12. Strunk A. QoS-Aware Service Composition: A Survey[C] Eighth IEEE European Conference on Web Services. IEEE Computer Society, 2010:67-74. [Google Scholar]
  13. Lei L, Dong Y. Multi-objective genetic optimization algorithm for SLA-aware service composition problem[J]. Journal of Jilin University, 2015, 45(1):267-273. [Google Scholar]
  14. Lin C H. Study of Optimizing Web Service Composition - Using Genetic Algorithm and Case-based Reasoning[J]. 2010. [Google Scholar]
  15. Todd S J. Genetic optimization method and system: US 9047569 B2[P]. 2015. [Google Scholar]
  16. Schuller D, Polyvyanyy A, García-Bañuelos L, et al. Optimization of Complex QoS-Aware Service Compositions. [C] Service-Oriented Computing -, International Conference, ICSOC 2011, Paphos, Cyprus, December 5-8, 2011 Proceedings. DBLP, 2011:452-466. [Google Scholar]
  17. Ye Z, Zhou X, Bouguettaya A. Genetic algorithm based QoS-aware service compositions in cloud computing[C] International Conference on Database Systems for Advanced Applications. Springer-Verlag, 2011:321-334. [Google Scholar]
  18. Mueller-Bady R, Kappes M, Palomo-Lozano F, et al. Maintaining Genetic Diversity in Multimodal Evolutionary Algorithms using Population Injection[C] on Genetic and Evolutionary Computation Conference Companion. ACM, 2016:95-96. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.