MATE: A Model-based Algorithm Tuning Engine

27 Apr 2020Mohamed El YafraniMarcella Scoczynski Ribeiro MartinsInkyung SungMarkus WagnerCarola DoerrPeter Nielsen

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... (read more)

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