论文标题
伴侣：基于模型的算法调整引擎
MATE: A Model-based Algorithm Tuning Engine
论文作者
论文摘要
在本文中，我们引入了一种基于模型的算法转动引擎，即伴侣，其中算法的参数表示为目标优化问题的特征的表达式。与大多数静态（独立于特征的）算法调谐引擎（例如IRACE和SPOT）相反，我们的方法旨在为特定问题得出给定算法的最佳参数配置，从而利用算法参数之间的关系和问题的特征。我们制定了找到参数与问题特征之间关系的问题，作为符号回归问题，我们使用遗传编程来提取这些表达式。为了进行评估，我们将方法应用于对OneMax，Leaders，Binvalue和跳跃优化问题的（1+1）EA和RLS算法的配置，其中理论上最佳的算法参数可作为问题的功能可用。我们的研究表明，发现的关系通常符合已知的理论结果，从而证明了一个新的机会，将基于模型的参数调整视为静态算法调谐引擎的有效替代方案。
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.
