  The ultimate goal of multi-objective optimisation is to help a decision maker
(DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs
among multiple conflicting criteria. This can be realised by leveraging DM's
preference information in evolutionary multi-objective optimisation (EMO). No
consensus has been reached on the effectiveness brought by incorporating
preference in EMO (either a priori or interactively) versus a posteriori
decision making after a complete run of an EMO algorithm. Bearing this
consideration in mind, this paper i) provides a pragmatic overview of the
existing developments of preference-based EMO; and ii) conducts a series of
experiments to investigate the effectiveness brought by preference
incorporation in EMO for approximating various SOI. In particular, the DM's
preference information is elicited as a reference point, which represents
her/his aspirations for different objectives. Experimental results demonstrate
that preference incorporation in EMO does not always lead to a desirable
approximation of SOI if the DM's preference information is not well utilised,
nor does the DM elicit invalid preference information, which is not uncommon
when encountering a black-box system. To a certain extent, this issue can be
remedied through an interactive preference elicitation. Last but not the least,
we find that a preference-based EMO algorithm is able to be generalised to
approximate the whole PF given an appropriate setup of preference information.
