Ensemble methods that combine a committee of machine‐learning models, each known as a member or base learner, have gained research interests in the past decade. One interest on ensemble generation involves the multi‐objective approach, which attempts to generate both accurate and diverse members that fulfill the theoretical requirements of good ensembles. These methods resolve common difficulties of balancing the trade‐off between accuracy and diversity and have been shown to be advantageous over single‐objective methods. This study presents an up‐to‐date survey on multi‐objective ensemble generation methods, including widely used diversity measures, member generation, selection, and integration techniques. Challenges and potential applications of multi‐objective ensemble generation are also discussed.WIREs Data Mining Knowl Discov2015, 5:234–245. doi: 10.1002/widm.1158This article is categorized under:Algorithmic Development > Ensemble Methods