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.

最近，人们越来越关注将模型驱动工程和基于搜索的软件工程结合起来。这种方法使用由作为模型转换实现的搜索运算符（模型变异器，有时是育种器）引导的元启发式搜索。这些运算符的设计可以显着影响元启发式搜索的有效性和效率。目前，设计搜索运算符由指定优化问题的人决定。然而，开发一致且高效的搜索运算符规则不仅需要领域专业知识，还需要有关优化的深入知识，这使得使用基于模型的元启发式搜索具有挑战性且成本高昂。在这篇论文中，我们提出了一种通用方法，可以为给定的优化问题自动生成保留原子多重性的搜索运算符。这减少了指定优化问题所需的工作，并使优化用户免受实现高效元启发式搜索变异算子的复杂性。我们通过一组案例研究评估我们的方法，并表明自动生成的规则与手动创建的规则相当，并且在某些情况下优于手动创建的规则，以指导进化搜索接近最佳解决方案。