MATE：一个基于模型的算法优化引擎
MATE: A Model-based Algorithm Tuning Engine
摘要：
在本文中，我们介绍了一个基于模型的算法转向引擎，即MATE，其中的算法参数表示为目标优化问题的特征的表达式。相反，大多数静态（功能独立）的算法调整引擎，如IRACE和SPOT，我们的方法的目的是获得一个给定的算法的最佳参数配置为一个特定的问题，利用算法参数和问题的功能之间的关系。我们制定的问题，发现参数和问题特征之间的关系作为一个符号回归问题，我们使用遗传编程来提取这些表达式。对于评估，我们将我们的方法应用于OneMax，LeadingOnes，BinValue和Jump优化问题的（1+1）EA和RLS算法的配置，其中问题的理论最佳算法参数可作为问题特征的函数。我们的研究表明，发现的关系通常符合已知的理论结果，从而展示了一个新的机会，考虑基于模型的参数调整作为一种有效的替代静态算法调整引擎。
英文摘要：
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.
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