Abstract
Interactive Evolutionary Systems (IES) can generate several designs based on a handful of input parameters. Never-theless, the choice of the parameters is an open problem and it is limited to a few evaluations as they require human input. As a solution, Bayesian Optimization (BO) can be used to tune IES
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