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A key challenge in the application of evolutionary algorithms in practice isthe selection of an algorithm instance that best suits the problem at hand.What complicates this decision further is that different algorithms may be bestsuited for different stages of the optimization process. Dynamic algorithmselection and configuration are therefore well-researched topics inevolutionary computation. However, while hyper-heuristics and parameter controlstudies typically assume a setting in which the algorithm needs to be chosenwhile running the algorithms, without prior information, AutoML approaches suchas hyper-parameter tuning and automated algorithm configuration assume thepossibility of evaluating different configurations before making a finalrecommendation. In practice, however, we are often in a middle-ground betweenthese two settings, where we need to decide on the algorithm instance beforethe run ("oneshot" setting), but where we have (possibly lots of) dataavailable on which we can base an informed decision.  We analyze in this work how such prior performance data can be used to inferinformed dynamic algorithm selection schemes for the solution of pseudo-Booleanoptimization problems. Our specific use-case considers a family of geneticalgorithms.

Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection