切换还是不切换：基于轨迹特征预测算法切换的效益
To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features
摘要：
动态算法选择的目的是通过在搜索过程中切换多个优化算法来利用它们的互补性。虽然这些类型的动态算法已被证明具有超越其组件算法的潜力，但仍不清楚如何最好地实现这种潜力。一种很有前途的方法是利用横向特性来实现基于每次运行的切换。在这里，第一种算法所看到的样本被用来创建一组特征，这些特征从算法的角度描述了景观。然后，这些特征用于预测切换到什么算法。在这项工作中，我们扩展了这种每次运行的基于故障的方法，以考虑各种各样的潜在点，在执行开关。我们发现，使用滑动窗口捕捉当地的景观功能包含的信息，可以用来预测是否在这一点上的开关将有利于未来的性能。通过分析产生的模型，我们确定哪些特征对这些预测最重要。最后，通过评估特征的重要性并比较多个算法之间的这些值，我们在第二种算法与切换前发现的局部景观特征的交互方式上显示出明显的差异。
英文摘要：
Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algorithms have been shown to have potential to outperform their component algorithms, it is still unclear how this potential can best be realized. One promising approach is to make use of landscape features to enable a per-run trajectory-based switch. Here, the samples seen by the first algorithm are used to create a set of features which describe the landscape from the perspective of the algorithm. These features are then used to predict what algorithm to switch to. In this work, we extend this per-run trajectory-based approach to consider a wide variety of potential points at which to perform the switch. We show that using a sliding window to capture the local landscape features contains information which can be used to predict whether a switch at that point would be beneficial to future performance. By analyzing the resulting models, we identify what features are most important to these predictions. Finally, by evaluating the importance of features and comparing these values between multiple algorithms, we show clear differences in the way the second algorithm interacts with the local landscape features found before the switch.
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