  In practical multi-criterion decision-making, it is cumbersome if a decision
maker (DM) is asked to choose among a set of trade-off alternatives covering
the whole Pareto-optimal front. This is a paradox in conventional evolutionary
multi-objective optimization (EMO) that always aim to achieve a well balance
between convergence and diversity. In essence, the ultimate goal of
multi-objective optimization is to help a decision maker (DM) identify
solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple
conflicting criteria. Bearing this in mind, this paper develops a framework for
designing preference-based EMO algorithms to find SOI in an interactive manner.
Its core idea is to involve human in the loop of EMO. After every several
iterations, the DM is invited to elicit her feedback with regard to a couple of
incumbent candidates. By collecting such information, her preference is
progressively learned by a learning-to-rank neural network and then applied to
guide the baseline EMO algorithm. Note that this framework is so general that
any existing EMO algorithm can be applied in a plug-in manner. Experiments on
$48$ benchmark test problems with up to 10 objectives fully demonstrate the
effectiveness of our proposed algorithms for finding SOI.
