Detecting Hidden and Irrelevant Objectives in Interactive Multi-Objective Optimization

Research output: Contribution to journalArticlepeer-review


Evolutionary multi-objective optimization algorithms
(EMOAs) typically assume that all objectives that are
relevant to the decision-maker (DM) are optimized by the EMOA.
In some scenarios, however, there are irrelevant objectives that
are optimized by the EMOA but ignored by the DM, as well as,
hidden objectives that the DM considers when judging the utility
of solutions but are not optimized. This discrepancy between the
EMOA and the DM’s preferences may impede the search for the
most-preferred solution and waste resources evaluating irrelevant
objectives. Research on objective reduction has focused so far on
the structure of the problem and correlations between objectives
and neglected the role of the DM. We formally define here the
concepts of irrelevant and hidden objectives and propose methods
for detecting them, based on uni-variate feature selection and
recursive feature elimination, that use the preferences already
elicited when a DM interacts with a ranking-based interactive
EMOA (iEMOA). We incorporate the detection methods into
an iEMOA capable of dynamically switching the objectives
being optimized. Our experiments show that this approach can
efficiently identify which objectives are relevant to the DM and
reduce the number of objectives being optimized, while keeping
and often improving the utility, according to the DM, of the best
solution found.
Original languageEnglish
JournalIEEE Transactions on Evolutionary Computation
Publication statusPublished - 2023


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