Generation of process using multi-objective genetic algorithm

The growing complexity of processes whatever their kind (i.e. business, software, medical, military) stimulates the adoption of process execution, analysis and verification techniques. However, such techniques cannot be accurately validated as it is not possible to obtain numerous and realistic process models in order to stress test them. The small set of samples and ``toy'' models publically available in the literature is usually insufficient to conduct serious empirical studies and thus, to validate thoroughly work around process analysis and verification. In this paper, we face this problem by proposing a process model generator using a multi-objective genetic algorithm. The originality of our approach comes from the fact that process models are built through a sequence of high-level operations inspired by the way a process modeler could have actually performed to model a process. A working generator prototype has been implemented and shows that it is possible to quickly generate huge, syntactically sound and user-tailored process models.