GP-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition

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

@InProceedings{Wang:2017:SEAL,

• author = "Chen Wang and Hui Ma and Aaron Chen and Sven Hartmann",
• title = "{GP}-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition",
• booktitle = "Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL-2017",
• year = "2017",
• editor = "Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin",
• volume = "10593",
• series = "Lecture Notes in Computer Science",
• pages = "170--183",
• address = "Shenzhen, China",
• month = nov # " 10-13",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-68759-9",
• URL = "https://doi.org/10.1007/978-3-319-68759-9_15",
• DOI = "doi:10.1007/978-3-319-68759-9_15",
• size = "14 pages",
• abstract = "Comprehensive quality-aware semantic web service composition aims to optimise semantic matchmaking quality and Quality of service (QoS) simultaneously. It is an NP-hard problem due to its huge search space. Therefore, heuristics have to be employed to generate near-optimal solutions. Existing works employ Evolutionary Computation (EC) techniques to solve combinatorial optimisation problems in web service composition. In particular, Genetic Programming (GP) has shown its promise. The tree-based representation used in GP is flexible to represent different composition constructs as inner nodes, but the semantic matchmaking information can not be directly obtained from the representation. To overcome this disadvantage, we propose a tree-like representation to directly cope with semantic matchmaking information. Meanwhile, a GP-based approach to comprehensive quality-aware semantic web service composition is proposed with explicit support for our representation. We also design specific genetic operation that effectively maintain the correctness of solutions during the evolutionary process. We conduct experiments to explore the effectiveness and efficiency of our GP-based approach using a benchmark dataset with real-world composition tasks.",

}

Genetic Programming entries for Chen Wang Hui Ma Aaron Chen Sven Hartmann

Citations