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Optimizing an algorithm's parameter set for evolutionary multi-objective optimization (EMO) algorithms is not performed regularly until now. However, it could have been learned from single-objective optimization that doing so yields remarkable improvements in algorithm's performance. Here, the sequential parameter optimization (SPO) framework is exemplarily applied to one EMO algorithm (EMOA) with...
The application of empirically determined surrogate models provides a standard solution to expensive optimization problems. Over the last decades several variants based on DACE (design and analysis of computer experiments) have provided excellent optimization results in cases where only a few evaluations could be made. In this paper these approaches are revisited with respect to their applicability...
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