The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajectories obtained from running a solver as input have shown promise. However, it is unclear to what extent these trajectories reliably discriminate between solvers. We propose a meta approach to generating discriminatory trajectories with respect to a portfolio of solvers. The algorithm-configuration tool irace is used to tune the parameters of a simple Simulated Annealing algorithm (SA) to produce trajectories that maximise the performance metrics of ML models trained on this data. We show that when the trajectories obtained from the tuned SA algorithm are used in ML models for algorithm-selection and performance prediction, we obtain significantly improved performance metrics compared to models trained both on raw trajectory data and on exploratory landscape features.

翻译：用于训练算法选择模型的输入数据选择被认为是模型成功的关键部分。近年来，使用求解器运行产生的短轨迹作为输入的无特征方法在算法选择中展现了潜力。然而，这些轨迹在多大程度上能可靠地区分不同求解器仍不明确。我们提出了一种元方法，用于生成针对求解器组合的判别性轨迹。利用算法配置工具irace来调整简单模拟退火算法（SA）的参数，以生成能够最大化基于此数据训练的机器学习模型性能指标的轨迹。研究表明，当将调优后的SA算法获得的轨迹用于算法选择和性能预测的机器学习模型时，与基于原始轨迹数据和探索性景观特征训练的模型相比，我们获得了显著改进的性能指标。