  Recommender systems are established means to inspire users to watch
interesting movies, discover baby names, or read books. The recommendation
quality further improves by combining the results of multiple recommendation
algorithms using hybridization methods. In this paper, we focus on the task of
combining unscored recommendations into a single ensemble. Our proposed method
is inspired by genetic algorithms. It repeatedly selects items from the
recommendations to create a population of items that will be used for the final
ensemble. We compare our method with a weighted voting method and test the
performance of both in a movie- and name-recommendation scenario. We were able
to outperform the weighted method on both datasets by 20.3 % and 31.1 % and
decreased the overall execution time by up to 19.9 %. Our results do not only
propose a new kind of hybridization method, but introduce the field of
recommender hybridization to further work with genetic algorithms.
