期刊
SOFTWARE AND SYSTEMS MODELING
卷 20, 期 6, 页码 1857-1887出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10270-021-00914-w
关键词
Model-driven optimisation; Search-based software engineering; Multi-objective optimisation
资金
- Engineering and Physical Sciences Research Council (EPSRC) [1805606]
There has been increased interest in combining model-driven engineering and search-based software engineering. A generalised approach was proposed to automatically generate search operators for a given optimisation problem, reducing the effort required to specify an optimisation problem. The automatically generated rules were shown to guide evolutionary search towards near-optimal solutions comparable to, and sometimes better than, manually created rules.
Recently, there has been increased interest in combining model-driven engineering and search-based software engineering. 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.
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