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Such approaches use meta-heuristic search guided by search operators (model mutators and sometimes breeders) implemented as model transformations. The design of these operators can substantially impact the effectiveness and efficiency of the meta-heuristic search. Currently, designing search operators is left to the person specifying the optimisation problem. However, developing consistent and efficient search-operator rules requires not only domain expertise but also in-depth knowledge about optimisation, which makes the use of model-based meta-heuristic search challenging and expensive. In this paper, we propose a generalised approach to automatically generate atomic multiplicity-preserving search operators for a given optimisation problem. This reduces the effort required to specify an optimisation problem and shields optimisation users from the complexity of implementing efficient meta-heuristic search mutation operators. We evaluate our approach with a set of case studies and show that the automatically generated rules are comparable to, and in some cases better than, manually created rules at guiding evolutionary search towards near-optimal solutions.<\/jats:p>","DOI":"10.1007\/s10270-021-00914-w","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T16:03:57Z","timestamp":1629129837000},"page":"1857-1887","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automatic generation of atomic multiplicity-preserving search operators for search-based model engineering"],"prefix":"10.1007","volume":"20","author":[{"given":"Alexandru","family":"Burdusel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Zschaler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"John","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"914_CR1","doi-asserted-by":"publisher","unstructured":"Abdeen, H., Varr\u00f3, D., Sahraoui, H., Nagy, A.S., Debreceni, C., Heged\u00fcs, \u00c1., Horv\u00e1th, \u00c1.: Multi-objective optimization in rule-based design space exploration. 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