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MATE: A Model-based Algorithm Tuning Engine
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.
On resampling vs. adjusting probabilistic graphical models in estimation of distribution algorithms
This step should be completed at each iteration of BOA, resulting in a very time-consuming process.
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