@misc{JohnKosiolLambersetal., author = {John, Stefan and Kosiol, Jens and Lambers, Leen and Taentzer, Gabriele}, title = {A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties}, series = {Software and Systems Modeling}, volume = {22}, journal = {Software and Systems Modeling}, issn = {1619-1374}, doi = {10.1007/s10270-022-01078-x}, pages = {1281 -- 1318}, abstract = {Optimization problems in software engineering typically deal with structures as they occur in the design and maintenance of software systems. In model-driven optimization (MDO), domain-specific models are used to represent these structures while evolutionary algorithms are often used to solve optimization problems. However, designing appropriate models and evolutionary algorithms to represent and evolve structures is not always straightforward. Domain experts often need deep knowledge of how to configure an evolutionary algorithm. This makes the use of model-driven meta-heuristic search difficult and expensive. We present a graph-based framework for MDO that identifies and clarifies core concepts and relies on mutation operators to specify evolutionary change. This framework is intended to help domain experts develop and study evolutionary algorithms based on domain-specific models and operators. In addition, it can help in clarifying the critical factors for conducting reproducible experiments in MDO. Based on the framework, we are able to take a first step toward identifying and studying important properties of evolutionary operators in the context of MDO. As a showcase, we investigate the impact of soundness and completeness at the level of mutation operator sets on the effectiveness and efficiency of evolutionary algorithms.}, language = {en} } @misc{TaentzerKosiolLambers, author = {Taentzer, Gabriele and Kosiol, Jens and Lambers, Leen}, title = {On the Application of Model-Driven Optimization to Business Processes}, series = {Lecture Notes in Computer Science}, journal = {Lecture Notes in Computer Science}, number = {14628}, publisher = {Springer}, doi = {10.1007/978-3-031-61433-0_2}, pages = {24 -- 35}, abstract = {The optimization of business processes is an important task to increase the efficiency of the described workflows. Metaheuristic optimization, such as evolutionary search, has been used to optimize business process models, but it requires a high level of expertise that not all process designers have. Model-driven optimization (MDO) promises to make the use of metaheuristic optimization accessible to domain experts without in-depth technical expertise by allowing them to specify the optimization algorithm directly at the model level. Because this approach is less technical, the process designers can focus on the business process models and their properties. Using concrete business process optimization problems as a starting point, we discuss how MDO can be applied to these problems, what MDO would offer for business process optimization, and how the application to business processes could stimulate research on MDO.}, language = {en} }