  We consider whether conditions exist under which block-coordinate descent is
asymptotically efficient in evolutionary multi-objective optimization,
addressing an open problem. Block-coordinate descent, where an optimization
problem is decomposed into $k$ blocks of decision variables and each of the
blocks is optimized (with the others fixed) in a sequence, is a technique used
in some large-scale optimization problems such as airline scheduling, however
its use in multi-objective optimization is less studied. We propose a
block-coordinate version of GSEMO and compare its running time to the standard
GSEMO algorithm. Theoretical and empirical results on a bi-objective test
function, a variant of LOTZ, serve to demonstrate the existence of cases where
block-coordinate descent is faster. The result may yield wider insights into
this class of algorithms.
