  The main goal of diversity optimization is to find a diverse set of solutions
which satisfy some lower bound on their fitness. Evolutionary algorithms (EAs)
are often used for such tasks, since they are naturally designed to optimize
populations of solutions. This approach to diversity optimization, called EDO,
has been previously studied from theoretical perspective, but most studies
considered only EAs with a trivial offspring population such as the $(\mu + 1)$
EA. In this paper we give an example instance of a $k$-vertex cover problem,
which highlights a critical difference of the diversity optimization from the
regular single-objective optimization, namely that there might be a locally
optimal population from which we can escape only by replacing at least two
individuals at once, which the $(\mu + 1)$ algorithms cannot do.
  We also show that the $(\mu + \lambda)$ EA with $\lambda \ge \mu$ can
effectively find a diverse population on $k$-vertex cover, if using a mutation
operator inspired by Branson and Sutton (TCS 2023). To avoid the problem of
subset selection which arises in the $(\mu + \lambda)$ EA when it optimizes
diversity, we also propose the $(1_\mu + 1_\mu)$ EA$_D$, which is an analogue
of the $(1 + 1)$ EA for populations, and which is also efficient at optimizing
diversity on the $k$-vertex cover problem.
