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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2014: Simulated Evolution and Learning pp 180–191Cite as

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

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8886)

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 unfeasible, 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.

Keywords

  • Genetic Programming
  • Service Composition
  • Functional Correctness
  • Genetic Programming Approach
  • Sequence Construct

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-13563-2_16
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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, New Zealand

    Alexandre Sawczuk da Silva, Hui Ma & Mengjie Zhang

Authors
  1. Alexandre Sawczuk da Silva
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  2. Hui Ma
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  3. Mengjie Zhang
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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da Silva, A.S., Ma, H., Zhang, M. (2014). A GP Approach to QoS-Aware Web Service Composition and Selection. In: , et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

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