Abstract
Process mining aims at discovering the workflow of a process from the event logs that provide insights into organisational processes for improving these processes and their support systems. Process mining abstracts the complex real-life datasets into a well-structured form known as a process model. In an ideal scenario, a process mining algorithm should produce a model that is simple, precise, general and fits the available logs. A conventional process mining algorithm typically generates a single process model that may not describe the recorded behaviour effectively. Multi-objective evolutionary algorithms (MOEA) for process mining optimise two or more objectives to generate several competing process models from the event logs. Subsequently, a user can choose a model based on his/her preference. In this paper, we have experimentally compared the popular second-generation MOEA algorithms for process mining.
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