  Since around 2000, it has been considered that elitist evolutionary
multi-objective optimization algorithms (EMOAs) always outperform non-elitist
EMOAs. This paper revisits the performance of non-elitist EMOAs for
bi-objective continuous optimization when using an unbounded external archive.
This paper examines the performance of EMOAs with two elitist and one
non-elitist environmental selections. The performance of EMOAs is evaluated on
the bi-objective BBOB problem suite provided by the COCO platform. In contrast
to conventional wisdom, results show that non-elitist EMOAs with particular
crossover methods perform significantly well on the bi-objective BBOB problems
with many decision variables when using the unbounded external archive. This
paper also analyzes the properties of the non-elitist selection.
