In this paper, we introduce a Model-based Algorithm Turning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configuration of a given algorithm for a specific problem, exploiting the relationships between the algorithm parameters and the features of the problem. We formulate the problem of finding the relationships between the parameters and the problem features as a symbolic regression problem and we use genetic programming to extract these expressions. For the evaluation, we apply our approach to configuration of the (1+1) EA and RLS algorithms for the OneMax, LeadingOnes, BinValue and Jump optimisation problems, where the theoretically optimal algorithm parameters to the problems are available as functions of the features of the problems. Our study shows that the found relationships typically comply with known theoretical results, thus demonstrating a new opportunity to consider model-based parameter tuning as an effective alternative to the static algorithm tuning engines.

翻译：在本文中,我们引入了一个基于模型的算法转换引擎, 即 MATE, 算法参数代表了目标优化问题特征的表达方式。 与大多数静态( 独立地) 算法调整引擎, 如 irares 和 SPOT 相比, 我们的方法旨在为特定问题找到特定算法的最佳参数配置, 利用算法参数和问题特征之间的关系。 我们将找到参数和问题特征之间的关系作为象征性回归问题, 我们用基因程序来提取这些表达式。 在评估中, 我们运用了我们的方法来配置 OneMax、 Beades One、 BinValue 和 跳跳动优化运算法, 这些问题的理论优化算法参数作为问题特征的函数。 我们的研究显示, 发现的关系通常符合已知的理论结果, 从而展示了考虑基于模型的参数调整作为静态算法调整引擎的有效替代方法的新机会 。