Chen Wang

PhD Student School of Engineering and Computer Science

Chen Wang profile picture

Thesis Info

Research Interests: Semantic Matchmaking, Semantic Web Service Composition, Genetic Programming, Estimation of Distribution Algorithm
Thesis Title: Comprehensive Quality-Aware Automated Semantic Web Service Composition
Supervisor: Dr Hui Ma and Dr Aaron Chen


Research Interests

Automated Web service composition is an NP-hard problem and it has been raising much attention in the research community due to the computational challenge and real-world applicability. Existing works either optimize QoS or semantic matchmaking quality, or are semi-automated approaches. The focus of our studies on service composition is to find effective and efficient approaches to comprehensive quality-aware semantic Web service composition, which aims to optimize semantic matchmaking quality and Quality of service (QoS) simultaneously. We will address this problem by achieving the following objectives: (1) developing EC-based approaches that explicitly support the comprehensive quality, (2) developing multi-objective approaches for optimizing the quality criteria involved in the comprehensive quality, (3) developing EC-based composition approaches for dynamic service composition while handling various changes of composition environment, and (4) developing EC-based approaches for semantic Web service composition supporting precondition and effects.


  1. WANG, C., MA, H., CHEN, A., AND HARTMANN, S. ''Comprehensive Quality-Aware Automated Semantic Web service Composition". AI 2017: Advances in Artificial Intelligence: 30th Australasian Joint Conference. 2017, pp. 195-207.
  2. WANG, C., MA, H., CHEN, A., AND HARTMANN, S. ''GP-Based Approach to Comprehensive Quality-aware automated semantic Web service composition". In: SEAL2017: International Conference on Simulated Evolution and Learning. 2017, pp. 170-183.
  3. WANG, C., MA, H., AND CHEN, A. "EDA-Based Approach to Comprehensive Quality-Aware Automated SemanticWeb Service Composition". In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2018 (GECCO ’18). (To appear)