Using Genetic Algorithms to Modularize Conceptual Models


Conceptual models often evolve into large and monolithic artifacts. This is a threat to comprehensibility and maintainability as the size of the resulting artefacts exceeds the cognitive processing capabilities of human beings. Breaking down monoliths into a modular structure is an established technique in software and systems engineering.



Part of this project is to realize Genetic Algorithms (GA) to automatically derive modularizations of given monolithic models. From a conceptual perspective, this project involves the challenge of encoding the problem and solution domain into a format, that can be used as an input for the GA. Moreover, a fitness function – most likely a multi-objective one – needs to be defined, based on domain-specific metrics, that yields the GA toward the good solutions. From a technological perspective, this project can extend an existing EMF and Jenetics based application or build a new one.

Possible applications can be on Entity Relationship models or other languages like BPMN, DMN, and ArchiMate.


Further Reading (Excerpt)

  • Moody, D. L., & Flitman, A. (1999). A methodology for clustering entity relationship models—a human information processing approach. In International Conference on Conceptual Modeling (pp. 114-130). Springer, Berlin, Heidelberg.
  • Further unpublished material will be provided via E-Mail