Fragment-based genetic programming for fully automated multi-objective web service composition

Web services have become increasingly popular in recent years, given their modular nature and reusability potential. A particularly promising application is in Web service composition, where multiple individual services with specific functionalities are composed to accomplish a more complex task. Researchers have proposed evolutionary computing techniques for creating compositions that are not only feasible, but also have the best possible Quality of Service (QoS). Some of these works employed multi-objective techniques to tackle the optimisation of compositions with conflicting QoS attributes, but they are not fully automated, i.e. they assume the composition workflow structure is already known. This assumption is often not satisfied, as the workflow is often unknown. This paper proposes a genetic programming-based method to automatically generate service compositions in a multi-objective context, based on a novel fragmented tree representation. An evaluation using benchmark datasets is carried out, comparing existing methods adapted to the multi-objective composition problem. Results show that the fragmented method has the lowest execution time overall. In terms of quality, its Pareto fronts are equivalent to those of one of the approaches but inferior to those of the other. More importantly, this work provides a foundation for future investigation of multi-objective fully automated service composition.