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.

使用机器学习方法来选择算法通常将描述一个实例的数据作为输入，我们提出了一种新颖的基于算法的方法来描述实例，该方法使用短的探测轨迹来训练算法选择模型，并且在计算效率和结果方面比传统的基于特征的方法更有前景。

论算法选择中探测轨迹的效用

On the Utility of Probing Trajectories for Algorithm-Selection

Numerical simulators are essential tools in the study of natural
fluid-systems, but their performance often limits application in practice.
Recent machine-learning approaches have demonstrated their ability to
accelerate spatio-temporal predictions, although, with only moderate accuracy
in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph
neural network model for learning to infer unsteady continuum mechanics in
problems encompassing a range of length scales and complex boundary geometries.
We demonstrate this method on advection problems and incompressible fluid
dynamics, both fundamental phenomena in oceanic and atmospheric processes. Our
results show good extrapolation to new domain geometries and parameters for
long-term temporal simulations. Simulations obtained with MultiScaleGNN are
between two and four orders of magnitude faster than those on which it was
trained.

介绍了一种基于图神经网络的多尺度模型 ——MultiScaleGNN，该模型可用于加速许多自然流体系统的模拟，且比传统数值模拟的速度更快。