  It is assumed in the evolutionary multi-objective optimization (EMO)
community that a final solution is selected by a decision maker from a
non-dominated solution set obtained by an EMO algorithm. The number of
solutions to be presented to the decision maker can be totally different. In
some cases, the decision maker may want to examine only a few representative
solutions from which a final solution is selected. In other cases, a large
number of non-dominated solutions may be needed to visualize the Pareto front.
In this paper, we suggest the use of a general EMO framework with three
solution sets to handle various situations with respect to the required number
of solutions. The three solution sets are the main population of an EMO
algorithm, an external archive to store promising solutions, and a final
solution set which is presented to the decision maker. The final solution set
is selected from the archive. Thus the population size and the archive size can
be arbitrarily specified as long as the archive size is not smaller than the
required number of solutions. The final population is not necessarily to be a
good solution set since it is not presented to the decision maker. Through
computational experiments, we show the advantages of this framework over the
standard final population and final archive frameworks. We also discuss how to
select a final solution set and how to explain the reason for the selection,
which is the first attempt towards an explainable EMO framework.
