Berger, Bernhard JohannesBernhard JohannesBergerPlump, ChristinaChristinaPlumpDrechsler, RolfRolfDrechsler2023-11-022023-11-022023IEEE Congress on Evolutionary Computation (CEC 2023)979-8-3503-1458-8https://hdl.handle.net/11420/44038Adapting optimisation algorithms, such as evolutionary algorithms, to a problem is a necessity. The required collection and exchange of domain information is an important but tedious task in real-world projects involving several experts from different areas of expertise (e.g. the domain and the optimisation area). This paper presents a structured approach that allows the experts to systematically provide their knowledge using domain-specific languages. The presented approach defines a different language for the involved experts that enables them to add their knowledge and use the information provided by other experts. The languages are extensible, allowing the addition of new optimisation aspects without changing the actual language. These languages are the front-end to a versatile open-source optimisation tool, that we built, enabling the actual execution of the optimisation. It additionally provides features for surrogate models as well as data generation and different benchmarks for evaluation. Conducting a user study, we show that the language is suitable to express the domain knowledge and domain experts can use the language to describe their domain knowledge after a short introduction. This way, the approach reduces the effort for domain experts in providing their information. As a side effect, the complete configuration of the optimisation execution through these languages allows an easy and reliable reproduction.endomain knowledgedomain-specific languageempirical evaluationEVOALgenetic algorithmsmachine learningsoftware engineeringMLE@TUHHComputer SciencesConference Paper10.1109/CEC53210.2023.10253985Conference Paper