  We present a framework to build a multiobjective algorithm from
single-objective ones. This framework addresses the $p \times n$-dimensional
problem of finding p solutions in an n-dimensional search space, maximizing an
indicator by dynamic subspace optimization. Each single-objective algorithm
optimizes the indicator function given $p - 1$ fixed solutions. Crucially,
dominated solutions minimize their distance to the empirical Pareto front
defined by these $p - 1$ solutions. We instantiate the framework with CMA-ES as
single-objective optimizer. The new algorithm, COMO-CMA-ES, is empirically
shown to converge linearly on bi-objective convex-quadratic problems and is
compared to MO-CMA-ES, NSGA-II and SMS-EMOA.
