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We investigate how combinations of evolutionary algorithms (portfolios) can be constructed efficiently for solving optimization problem instances drawn from a distribution. We consider selection methods ranging in intricacy and based on different principles including a technique for efficient tuning of metaheuristics, a racing algorithm. The selectors are used here to optimize instances of the Preventive...
We present a test suite of 23 instances of a preventive maintenance scheduling problem from the power industry, which we also make available online. The formulation of the problem and the suite are derived from real-world data collected recently. A first study of the landscape characteristics of these problem instances based on three different types of adaptive walk reveals a generally rugged landscape,...
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