The goal of automatic algorithm configuration is to recommend good parameter settings for an algorithm or solver on a per-instance basis, i.e., for the specific problem instance being solved. Realtime algorithm configuration is a practically motivated variant of algorithm configuration, in which the problem instances arrive in a sequential manner and high-quality configurations must be chosen during runtime. We model the realtime algorithm configuration problem as an extended version of the recently introduced contextual preselection bandit problem. Our approach combines a method for selecting configurations from a pool of candidates with a surrogate configuration generation procedure based on a genetic crossover procedure. In contrast to existing methods for realtime algorithm configuration, the approach based on contextual preselection bandits allows for the incorporation of problem instance features as well as parameterizations of algorithms. We test our algorithm on different realtime algorithm configuration scenarios and find that it outperforms the state of the art.