Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance description or fitness landscape, or can be a direct representation of the instance itself, i.e. an image or textual description. Regardless of the choice of input, there is an implicit assumption that instances that are similar will elicit similar performance from algorithm, and that a model is capable of learning this relationship. We argue that viewing algorithm-selection purely from an instance perspective can be misleading as it fails to account for how an algorithm `views' similarity between instances. We propose a novel `algorithm-centric' method for describing instances that can be used to train models for algorithm-selection: specifically, we use short probing trajectories calculated by applying a solver to an instance for a very short period of time. The approach is demonstrated to be promising, providing comparable or better results to computationally expensive landscape-based feature-based approaches. Furthermore, projecting the trajectories into a 2-dimensional space illustrates that functions that are similar from an algorithm-perspective do not necessarily correspond to the accepted categorisation of these functions from a human perspective.

翻译：机器学习方法在算法选择中通常以描述实例的数据作为输入。输入数据可以是实例描述或适应度景观中提取的特征，也可以是实例本身的直接表示，如图像或文本描述。无论选择何种输入，都存在一个隐含假设：相似的实例会引发算法产生相似的表现，并且模型能够学习这种关系。我们认为，纯粹从实例角度看待算法选择可能具有误导性，因为它未能考虑算法如何“看待”实例间的相似性。我们提出一种新颖的“以算法为中心”的实例描述方法，可用于训练算法选择模型：具体而言，我们使用通过将求解器应用于实例极短时间计算得到的短探测轨迹。该方法展现出了潜力，能够提供与计算成本高昂的基于景观的特征方法相当或更优的结果。此外，将轨迹投影到二维空间表明，从算法视角相似的函数，并不一定对应于人类视角下对这些函数的公认分类。