On the automatic generation of metaheuristic algorithms for combinatorial optimization problems
提出一种自动设计元启发式算法的方法，通过灵活组件框架和自动配置生成算法，在三个不同问题上超越先前研究，帮助研究者快速获得高性能算法。
Metaheuristic algorithms have become one of the preferred approaches for solving optimization problems. Finding the best metaheuristic for a given problem is often difficult due to the large number of available approaches and possible algorithmic designs. Moreover, high-performing metaheuristics often combine general-purpose and problem-specific algorithmic components. We propose here an approach for automatically designing metaheuristics using a flexible framework of algorithmic components, from which algorithms are instantiated and evaluated by an automatic configuration method. The rules for composing algorithmic components are defined implicitly by the properties of each algorithmic component, in contrast to previous proposals, which require a handwritten algorithmic template or grammar. As a result, extending our framework with additional components, even problem-specific or user-defined ones, automatically updates the design space. Furthermore, since the generated algorithms are made up of components, they can be easily interpreted. We provide an implementation of our proposal and demonstrate its benefits by outperforming previous research in three distinct problems from completely different families: a facility layout problem, a vehicle routing problem and a clustering problem.